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This is to certify that the

dissertation entitled

FACTORS AND TRENDS OF REGIONAL SHIFTS OF PRODUCTION:
ANALYSIS OF THE U.S. PORK SECTOR ‘

presented by

Bishwa Bhakta Adhikari

has been accepted towards fulfillment
of the requirements for

Ph.D. Agricultural Economics

 

 

degree in

 

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Major professor

Date December 13, 2002

 

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FACTORS AND TRENDS OF REGIONAL SHIFTS OF PRODUCTION:
ANALYSIS OF THE U.S. PORK SECTOR
By

Bishwa Bhakta Adhikari

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Agricultural Economics

2002

 

ABSTRACT
FACTORS AND TRENDS OF REGIONAL SHIFTS OF PRODUCTION:
ANALYSIS OF THE U.S. PORK SECTOR
By

Bishwa B. Adhikari

Until the mid 2001 century, the pork industry in the United States was
characterized by production on numerous unspecialized small farms scattered across the
rural landscape. The pork industry in the recent past has transferred into fewer, larger
and specialized operations. The phenomenon of change is continuous. Historically,
input availability, development of transportation systems, technological changes in
production systems, government regulations and the consumer preferences have been
driving changes in the pork industry. All these forces affect the competitiveness of one
region relative to other regions. This dissertation examines the historical trends in the
U.S. hog production and regional shifts of hog operations from traditional regions to the
new regions. Relevant literature has been reviewed to identify the factors of regional
shift. Three major factors: pork demand, variation in cost of production and processing,
and government regulations are discussed in detail. The analysis is focused on how these
forces affect the regional competitiveness of the pork industry and movement towards
larger, specialized and geographically concentrated operations.

The pork industry, as influenced by its background is reviewed first. Trends in
pork production, processing, and marketing and factors that may be directly or indirectly

impacting industry structure are summarized. The theoretical aspects of meat demand are

 

discussed and regional pork demands are estimated using an econometric model. Costs
of finishing pigs in different regions are estimated for small, medium and large operations
in order to allow examination of competitiveness of feeding operations by size of
operation. The study also analyzes the differences in federal and state regulations and
develops an index to compare the relative stringency by production regions. Various
technology options in manure management are briefly discussed. Based on these options
and United States Environment Protection Agency data, compliance costs are analyzed
for different sizes of operations. Finally, a mathematical programming model is used to
analyze the effect of market forces on the pork industry structure.

The results of this study show that raising hogs in larger operations is less costly.
Small-sized operations in some regions are still required to produce hogs to meet the
demand for consumption and export. Although environmental compliance cost is
considered one of the major factors of industry relocation, the analysis showed that the
effect of such costs was minimal. Feed costs and transportation costs play a great role in
location of production and processing.

The results also revealed that pork operations tend to locate near the populous
areas to meet the consumer demand and at the same time minimize the transportation
cost. Pressures fi'om current and fiiture environmental regulations, moratoria and
scarcity of agricultural land for manure management tend to keep the hog operations
away fiom high population areas. A future scenario analysis suggested that the western
region of the U.S. would experience higher growth in pork production by the year 2010.
The current trend of fewer and larger production units and location changes in the pork

industry will continue in the future.

 

Copyright by:
Bishwa B. Adhikari
2002

 

This work is dedicated to my parents, Mana Harka and Apsara Adhikari

 

ACKNOWLEDGEMENT

The completion of this dissertation was made possible with the support and
assistance of several individuals. I would like to extend my sincere thanks to Dr. Laura
M. Cheney, who as my major professor, guided me in the PhD program and provided
continuous support during my course work and the research. I especially acknowledge
and appreciate the patient leadership and guidance provided by Dr. Stephen B. Harsh as a
dissertation supervisor. Helpful suggestions from Dr. Gerald Schwab and Dr. Patricia
Norris, and Dr. Kellie Raper were instrumental in improving the quality of the work. I
would like to acknowledge Dr. James Hilker, who served as my major professor in my
MS program and encouraged me to continue for the PhD degree at Michigan State
University. Thanks to Mr. Leroy Stodick, computer analyst at University of Idaho, for
helping in computer modeling.

I appreciate Dr. Murari Suvedi, Mrs. Yesoda Suvedi and Dipendra Subedi for
continuous inspiration and moral support in my academic endeavor. Special thanks are
due to my wife, Sita, my children, Bipal, Ritu and Richa for their patience and
understanding. Without their cooperation, it would have been impossible to accomplish

my goal.

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. xi

LIST OF FIGURES ........................................................................................................ xiii
CHAPTER 1

Introduction ......................................................................................................................... 1

1.1 Industrialization of agriculture ............................................................................... 4

1.2 Industrialization of the U.S. pork sector ................................................................ 6

1.3 Statement of problem ............................................................................................. 9

1.4 Objectives ............................................................................................................. 10

1.5 Organization of dissertation ................................................................................. 1 1
CHAPTER 2

Literature Review .............................................................................................................. 13

2.1 Industry structure .................................................................................................. 13

2.1.2 Historical perspectives ................................................................................... 15

2.1.3 Vertical coordination system ......................................................................... 17

2.1.3.1 Spot market .............................................................................................. 18

2.1.3.2 Contracts .................................................................................................. 18

2.1.3.3 Strategic alliances .................................................................................... 19

2.1.3.4 Formal cooperation .................................................................................. 19

2.1.3.5 Vertical integration .................................................................................. 19

2.1.4 Coordination in pork sector ........................................................................... 20

2.1.5 Technology induced structural change .......................................................... 21

2.1.6 Government regulations and pork industry .................................................... 24
CHAPTER 3

Pork industry: trends in production and firm locations .................................................... 25

3.1 Trend in number of hog farms and production in the U.S. .................................. 25

3.2 Increasing geographic concentration of production ............................................. 31

3.3 Factors affecting location of production .............................................................. 35

3.3.1 Technological change .................................................................................... 35

3.3.2 Corporate farming laws .................................................................................. 36

3.3.3 Property values ............................................................................................... 36

3.3.4 Economic options ........................................................................................... 37

3.3.5 Environmental adsorptive capacity ................................................................ 38

3.3.6 Public polICIes .......... 38

3.3.7 Consumer demand ......................................................................................... 38

3.3.8 Contractual arrangements .............................................................................. 39

3.3.9 Agglomeration ............................................................................................... 40

vii

CHAPTER 4

Approximation of pork demand ........................................................................................ 41

4.1 Introduction ......................................................................................................... 42

4.2 Theoretical background ........................................................................................ 42

4.3 Market demand ..................................................................................................... 47

4.4 Demand model specification ................................................................................ 48

4.5 Demand system approximation and application .................................................. 50

4.5.1 Pork demand approximation .......................................................................... 50

4.6 Empirical estimation of pork demand .................................................................. 54

4.7 Pork export demand ............................................................................................. 59

4.8 Pork import ........................................................................................................... 62
CHAPTER 5

Regional competitive position of pork industry ................................................................ 63

5.1 Economics of production ..................................................................................... 63

5.2 Feed supplies and hog production ........................................................................ 65

5.3 Pig feeding Operations budgets (grow to finish) .................................................. 68

5.3.1 Assumptions made in enterprise budgeting ................................................... 69

5.4 Enterprise budgets: Feeder to finish operations ................................................... 70

5.4.1 Formulation of pig diets ................................................................................. 70

5.4.2 Enterprise budgets by regions and size of Operations .................................... 77

5.5 Pork processing industry in regional competition ................................................ 86

5.6 Locations of pork processing plants ..................................................................... 88

5.6.1 Pork processing cost ...................................................................................... 91
CHAPTER 6

Environmental regulations and regional competition in the hog industry ........................ 93

6.1 Hog production and manure management ........................................................... 93

6.2 Regulations and hog industry relocation .............................................................. 95

6.3 Confined animal feeding operations and state regulations ................................... 96

6.4 Description of technology options for manure management ............................. 107
CHAPTER 7

Optimization of production and processing of pork ....................................................... 1 13

7.1 Mathematical programming: economic environment ........................................ 114

7.2 Mathematical model ........................................................................................... 115

7.3 Transshipment model set up ............................................................................... 118

7.3.1 Production regions ....................................................................................... 118

7.3.2 Processing regions ....................................................................................... 121

7.3.3 Pork consumption regions (markets) ........................................................... 122

7.3.4 Transportation cost ....................................................................................... 123

7.4 Transshipment model tableau ............................................................................. 124

viii

CHAPTER 8

Results and discussion .................................................................................................... 127
8.1 Optimum production level by region ................................................................. 127
8.2 Optimum level of pork processing by region ..................................................... 130
8.3 Pork demand and shadow prices ........................................................................ 134
8.4 Industry implications .......................................................................................... 136
8.5 Scenarios analysis .............................................................................................. 138
8.5.1 Increase in pork demand .............................................................................. 138
8.5.2 Expansion of production .............................................................................. 139
8.5.3 Expansion of processing capacity in the West ............................................. 140
8.5.4 Increase in compliance costs ........................................................................ 140
8.5.5 Results of the scenario analysis ................................................................... 141
CHAPTER 9
Summary amd conclusion ........................................................................................ 147
APPENDICES
Appendix 1: Source of data ............................................................................................. 152
Appendix 3 ...................................................................................................................... 153
Appendix 3. 1 America’s top 25 hog producing counties ........................................... 153
Appendix 4 ...................................................................................................................... 154
Appendix 4.1 U.S. per capita meat consumption and prices of meats ........................... 154
Appendix 4.2 Meat Expenditure Shares ..................................................................... 155
Appendix 4.3 Regression analysis of per capita pork demand .................................... 156
Appendix 4.4 Approximated pork demand (pounds) by states ................................... 160
Appendix 4.5 U.S. agricultural exports: Live animals and meats ............................... 162
Appendix 5 ...................................................................................................................... 163
Appendix 5.1 Hog and pigs production and marketing ............................................... 163
Appendix 5.2 Production of barley, sorghum and corn grain in selected states .......... 164
Appendix 5. 3 Comparison of wage rates and processing costs by selected states ..... 165
Appendix 5.4 Average prices of inputs and market hogs in selected States ............... 166
Appendix 5.5 Suggested high nutrient density diets for growing swine ................ Error!
Bookmark not defined.167
Appendix 5.6A Production costs and return (large scale operations) ........................ 168
Appendix 5.6B Production costs and return (medium scale operations) ................... 169
Appendix 5.6C Production costs and return (small scale operations) ....................... 170
Appendix 6 ...................................................................................................................... 171
Appendix 6.] Data and procedure for compliance cost estimation ............................. 171
Appendix 6.2 Regulatory compliance costs for swine ............................................... 173
Appendix 6.3 Environmental compliance cost per pig ............................................... 197
Appendix 6.4 Environmental compliance costs by states and regions ........................ 198
Appendix 7 ...................................................................................................................... 199
Appendix 7.1 Shipping cost as a function of volume and distance ............................. 199
Appendix 7.2 Minimizing total cost of production, processing and transportation 200
Appendix 8 ...................................................................................................................... 209
Appendix 8.1 Production levels and shadow prices in optimal solution ..................... 209

 

Appendix 8.2 Pork processing locations and destinations ......................................... 211
Appendix 8.3 Pig flow from production locations to processing ................................ 212
Bibliography .........................................................................................................................

 

Appendix 8.2 Pork processing locations and destinations ......................................... 211
Appendix 8.3 Pig flow from production locations to processing ................................ 212
Bibliography .........................................................................................................................

 

LIST OF TABLES

Table 3.1 Trends in number and size of hog farms 26
Table 3.2 Number of operations and hog inventory in selected states 29
Table 3.3 Number of farms and per farm pig production 30
Table 4.1 Goodness of fit of the models in predicting pork demand 52
Table 4.2 Comparision of percapita pork consumption estimates by different models 53
Table 4.3 Parameter estimates for pork demand by Rotterdam model 55
Table 4.4 Quantities of pork consumption by regions and selected age/sex groups 57
Table 4.5 Estimated regiOnal pork demands 58
Table 4.6 Pork export to selected countries 60
Table 4.7 U.S. pork net exports 62
Table 5.1. Average prices of inputs in different regions 68
Table 5.2 Growing-finishing: feed usage by pig growth rate 72
Table 5.3 Suggested diets for finishing swine using corn as the major grain source 73
Table 5.4 Hogs inventories by size of operations in selected states 78
Table 5.5a Feeder to finish system: cost and return per 100 hogs, E. Corn Belt 79
Table 5.5b Feeder to finish system: cost and return per 100 hogs, W. Corn belt 80
Table 5.5c Feeder to finish system: cost and return per 100 hogs, South 81
Table 5.5d Feeder to finish system: cost and return per 100 hogs, Northeast 82
Table 5.5c Feeder to finish production system: cost and return per 100 hogs, west 83
Table 5.6 Estimated opportunity cost of unpaid family labor 85
Table 5.7 Cost of production comparisons by pork production system 85
Table 5.8 Plant capacities of the five largest slaughter firms 88
Table 5.9 Estimated daily slaughter capacities in different pork processing plants. 89
Table 5.10 Regional distribution of pork processing capacity 91
Table 5.11 Regional pork processing costs 92
Table 6.1 Descriptions of federal and state stringency 97
Table 6.2. a: Stringency on livestock feeding operations 100
Table 6.2. b: Stringency on livestock feeding operations contd.. 102
Table 6.3 Environmental stringency grouping 105
Table 6.4 Management techniques required by swine operations by region“ 109
Table 6.5 Technology options for CAFOS and compliance costs 110
Table 6.6 Compliance costs by production region 112
Table 7. 1 Production regions and number of hogs marketed 120
Table 7.2 Annual maximum hog slaughtering capacity in different regions 121
Table 7. 3 Regional demarcation and quantity of pork demanded in 1,000 lbs 122
Table 7. 4 A simple transshipment-programming tableau 125
Table 8.1 Regional allocation of production by size of operations 129
Table 8.2 Pattern of pig flow in the optimum solution 131
Table 8.3 Locations and optimal levels of processing 131
Table 8.4 Shipment of pork from processing regions to the markets 133
Table 8.5 Market demand (Mil. Pounds) and shadow prices 135
Table 8.6 Optimum level of pork production (Year 2010) 141
Table 8.8 Pattern of pig flow (Year 2010) 143
Table 8.9 Locations and levels of processing (Year 2010) 144

Table 8.10 Pattern of pork flow in optimum solution (Year 2010) 145

xi

 

Table 8.11 Demands and shadow prices of pork (Year 2010) 146

xii

 

LIST OF FIGURES

Figure 1.1:
Figure 1.2:
Figure 2.1:
Figure 3.1:
Figure 3.2:
Figure 3.3:
Figure 3.4:
Figure 3.5:
Figure 4.1:
Figure 4.2:
Figure 5.1:
Figure 5.2:
Figure 5.3:
Figure 5.4:
Figure 6.1:

Share of cash (spot) market (1994-2000)

Product flow and feedback channels in the pork industry
Conceptual structural change model

The United States of America and geographical regions
Trends in pork production and number of pig farms in the U.S.
Distribution of hogs in contiguous U.S. 1982 and 1999

Farms with 200 or more hogs and pigs inventory in 1997
Hogs and pigs sold by counties in 1997

Estimated and observed per capita pork consumption (1972-1999)
Annual U.S. pork export demand (1989-1997)

Short-run equilibrium with three different firms

Approximate optimum temperature zones for pigs

Ventilation curves for pig feeding operation.

Old vs. modern processing plants:

Environmental stringency grouping

xiii

23
27
31
32
34
34
56
61
65 ,
76
77
87
106

 

Chapter 1
I. Introduction
The U.S. pork industry is an important value-added sector in the agricultural economy.
Annual farm sales (market hogs sold) usually exceed $11 billion, while the annual retail
value of pork sold to consumers exceeds $30 billion. The pork industry supports over
600,000 jobs and adds approximately $27 billion in value to basic production inputs such
as soybean and corn (National Pork Producers Council, 1999). The total U.S. hog
population is about 60 million animals, with about 68 percent located in the Corn Belt
area, where they have access to abundant supplies of feed grains and soybean meal.
Another 20 percent of hogs are produced in the Southeast (Economic Research Service,
2000).

The pork industry is a complex system of producing, marketing, processing and
distributing of pork and pork products. The production process uses many inputs (e. g.,
feedstuff, labor, capital, land, etc.) to produce live hogs. Similarly, processing and
marketing processes require capital, labor and several other inputs. In the 19708 and
19805, hogs were generally produced on farrow-to-finish farms. Although farrow-to-
finish farms are still utilized, recently, hog production has shifted to specialized farms at
four distinct sites, usually separated by location.

0 F arrow-to-wean operation: Breeds pigs and ships 10 to 15 pound pigs to nursery
operations.
0 Farrowing-nursery operation: Breeds pigs and ships 40- to 60-pound “feeder” pigs

to growing-finishing operations.

0 Nursery Operation: Manages weaned pigs (more than 10 to 15 pounds) and ships
40- to 60-pound “feeder” pigs to growing-finishing operations. The final product
from nursery operation is the same as from the farrowing-nursery operation.

0 Grow-finishing/feeder-to-finish operation: Handles 40 to 60 pound pigs and
finishes these to market weights of about 250 to 265 pounds.

After pigs reach a weight of about 250 pounds, producers sell them at terminal markets or
sell them directly to packers. Nineteen large packers surveyed in 1993 indicated that 87
percent of hog supplies came from the spot market (Hayenga et al., 1996). However,
recently the market coordination method has changed dramatically. About two-thirds of
all the hogs were sold to packers under a marketing contract in 1999 (Fig 1.1). Producers
are increasingly shifting to contracts to decrease risks in the spot market, and packers are
willing to offer these contracts to get the number and quality of hogs required. As
presented in Fig. 1.1, the share of cash (spot) market transaction has declined sharply in
recent years.

Sixty-two percent of total hogs marketed were sold in spot market in 1994 and the share
decreased to 26 percent in the year 2000. If this trend is continued, contracts will

eliminate the cash market (Feed-Stuff, March 13, 2000).

Figure 1.1: Share of cash (spot) market (1994-2000)

 

Hog: marketed in cash market [1994-2030]

 

 

 

 

 

PE rce I1! of flags

 

 

 

 

1994 1997 1999 2000

Year

 

 

 

Source: Compiled from Feed-Stuff, March 13, 2000

There are two distinct production and marketing channels in the U.S. pork
industry as represented by Figure 1.2. The first channel targets the specific product
markets and the commodity markets are targeted by the second. Solid arrows indicate the
product flow and broken arrows reflect feedback loops.

Both products oriented and commodities oriented production and
marketing channels are in existence in the U.S. pork sector. Industrialized producers with
processing and packing facilities dominate the specialty side, whereas independent
producers without processing and packing facilities dominate the commodity side. The
spot market is the dominant method of pricing in the commodity hog channel. The trend

in U.S. agricultural production has been turning away from commodity production and

towards product specialization.

Figure 1.2: Product flow and feedback channels in the pork industry

 

 

Breeding/Genetic Companies

 

i:

ii

 

Producers

 

 

 

Producers

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0 Nursery 0 Nursery
0 Finishing - Finishing
i + i 9
Product Commodity
Slaughter/Processing Slaughter/Processing
l 4 i 4
Specific Product Commodity Markets
Markets (Label) (Generic, non-label)

 

 

 

 

H

 

 

ii

 

 

Consumers

 

(Product flow and feedback)

 

1.1 Industrialization of agriculture

The changing nature of linkages between stages and consolidation of firms in the
food production and distribution system has been referred to as the industrialization of
agriculture (Boehlje and Schrader, 1998). Food production and distribution systems are
experiencing structural changes. Linkages between the producers, input suppliers, and
product buyers are important elements of structural change. In the production of
agricultural products, the number of linkages has increased. “Most agricultural producers
are sourcing more inputs from outside the farm and performing fewer activities or
processes along the chain that result in the final food product” (Boehlje and Schrader,
1998).

According to the U.S. census data (1997), a typical U.S. farm produced just one
or two farm products, whereas 90 percent of all farms in 1920 raised chickens, 75 percent
raised at least one pig and 69 percent milked at least one cow. In 1997, just 5.3 percent
farms raised pigs and 6.1 percent kept cows. Twenty chicken producers control 85
percent of all chicken production today, 3.5 percent of all cow/calf producers control 33
percent of the cow herd, two percent of all feedlots feed 85 percent of the feedlot
inventory, seven percent of all dairy producers milk 59 percent of the dairy herd and

seven percent of all hog producers produce 70 percent of the country's hogs.

1.2 Industrialization of the U.S. pork sector

During the 19503, the broiler industry made its dramatic structural change towards
vertical integration. Agricultural economists and industry experts believed that the pork
industry would follow the broiler industry’s vertical integration model. Traditional hog
producers disliked this model and hog production has deviated from the broiler model
(Rhodes, 1995). However, currently the structure of the U.S. pork industry is in rapid
transition. During the 19805 and 19903, major pork industry related technological
advances benefited the pork industry. These advances allowed production to grow
significantly in states not known previously for pork production. These technological
advances resulted in cost efficiency by achieving a lower average cost of production and
processing.

Applying new technology to existing firms may not be the best option].
Sometimes it is more efficient to start with complete new production units in order to
capture the full benefits Of the new technologies. As an analogy, consider the cost
involved with upgrading an older version of a personal computer versus buying a newer
version of a computer. Depending upon such factors as the age of the computer, the
technology available, and the salvage value of the old computer; such a decision to
replace may be Optimal. It is often as expensive to upgrade a computer, as it is to buy a

new one. Moreover, a new computer may have more capacities and computational power

 

1. Many small and mid-size Midwest production facilities are of a size and technology
that can continue to produce if capital and investment costs have already been recovered,
but will not likely be profitable if major remodeling or upgrading of investments is
necessary to remain in Operation. Because of technological, size, environmental, or
managerial conditions and limitations, many of these production facilities are likely to be

 

than the upgraded one. Similarly, much of the new pork production technologies cannot
be fully implemented using the existing physical and human resources in traditional hog
areas (Hurt et al., 1995). Technological advances lead to new types of production and
processing facilities. This change encourages shifts of location to regions with advantages
in the new types of units (Gillespie, 1996).

Large-scale hog production that utilizes new technologies has increased in the
southern and western parts of the United States. These regions have competitive
advantages in adopting the new types of production units. New operations have better
arrangements with feed mills, packers, and other contractors to reduce production costs
and improve risk management. Large operations have advantages over smaller operations
(e.g., economies of scale). Lower cost and product differentiation are two basic types of
competitive advantages (Porter, 1990), which encourage the shift to larger scale pork
operations. These economic factors directly or indirectly contribute to the profitability Of
the industry and therefore drive spatial shifts of operations.

Locations where Operating costs are lower and a favorable labor climate exists
(i.e. high labor productivity, positive work attitude and low wage rates), can be dominant
considerations for location decisions. Feed grains are the primary input for hog
production and the transportation costs of bulky and heavy feed grains are generally high.
Proximity to feed suppliers influences location decisions. Similarly, proximity to the
markets is another important consideration since products of pork industry are bulky and

involve high transportation costs.

 

phased out of production rather than upgraded and modernized in place (Boehlje and
Schrader, 1998)

 

Hog operations in the U.S. are not only getting larger, but are also moving to non-
traditional pork states such as North Carolina, Arkansas, Utah, and Colorado where
production has increased substantially. Production of hogs in North Carolina (currently
the state with the second largest hog inventory) increased by 278 percent from 1987 to
1997. Iowa, still the number one state in terms of hog inventory, increased hog
production by only 13 percent in the same period. In contrast, Illinois, formerly the
second highest-ranking state in 1987, decreased its production by 17 percent (Table 3.2).
The explosive growth of the hog industry, particularly in North Carolina and changes in
industry structure, have raised the issue of social, economic and environmental
sustainability with respect to the location and long term viability of the industry.

Expansion of the hog industry in the Southeast region (non-traditional region)
may also slow in the future. The growing hog business and its malodorous by-products
are raising eyebrows of regulators and environmentalists. Constraints such as higher costs
associated with management of odor, flies and manure are important considerations in the
hog industry expansion.

Consumer demand for more processed and specialized foods is another driving
force for structural changes. Consumer preferences have changed toward meat products
that are leaner, more consistent, and more convenient to prepare. Pork production and
processing firms have built new alliances with hog breeders and producers to ensure
breeding and production decisions that yield a superior product and meet consumer
needs. This alliance results in an industry with a supply chain structure, where hogs are
grown under contracts or by large integrated firms (Drabenstott, 1998). Vertical

integration and contracts in production and marketing have become prominent in the pork

industry, facilitating the transmission of consumers demand to the producers (Hennessy,
1996)

Public policies along with new technologies, and a favorable business climate are
a few of the forces driving such changes (Gillespie et al., 1997). Public policies can
encourage or discourage current or future market behaviors. Grants and subsidies, for
example, provide incentives whereas regulations and standards are disincentives (Seidl
and Grannis, 1998). Similarly, taxes, zoning, quotas, permits, research, and education are
examples of public policy tools that play important roles in industry structure and

performance.

1.3 Statement of problem

Pork producing operations in the U.S. are moving from the Corn Belt (traditional
regions) to the Southeast, West and Southwest. In addition to spatial movement, the hog
operations are growing in size, but shrinking in ntunber. The trend of fewer but larger
farms raising more hogs has been continuous for the last 50 years. This structural change
affects farm communities, the environment, and pork consumers. The effect of the
change has both positive and negative impacts on consumers and producers. Per unit cost
of production has gone down lowering the price of pork for consumers. However,
smaller producers may not be able to compete with larger producers, which would lead to
further concentration in production. A study of the current market structure, economic
motivations, and environmental constraints of the pork industry is required to model the
regional distribution of hog operations. It is important to analyze the trends and factors

Of regional shifts of U.S. hog production so that policy makers and industry leadership

will understand recent changes in pork production, and better anticipate further changes

in the industry.

1.4 Objectives

1. Objective: To review the present supply and demand situation of the U.S. pork
industry.
Related Concerns:
o What regional differences are there with respect to cost of pork production and
processing?
0 What regional differences are there with respect to demand for pork?
2. Objective: To study recent regional shifts in the U.S. pork industry.
Related Concerns:
o What are the temporal and spatial patterns Of regional shifts in pork production
and processing?
3. Objective: To predict the future locations of pork production and processing
operations.
Related Concerns:
- What factors influence location of pork production and processing?
0 What are the best locations for production and processing of hogs based on
factors influencing supply and demand?
0 Will the pork production and processing facilities continue to operate in existing

locations?

11“

can .u.

1.5 Organization of dissertation

This study does not involve original data gathering or surveys, rather secondary
data from different sources, particularly U.S. government documents, are used. The
dissertation is organized into nine chapters. Chapter One is devoted to the introduction of
the U.S. pork industry, problem statements and objectives and related concerns of the
study. Chapter Two provides a literature review in which the concept of structural
changes in agriculture including the pork industry is introduced. Chapter Three discusses
the pork industry’s historical perspective and recent trends in pork production,
processing, and marketing. This chapter also summarizes the factors that might be
directly or indirectly responsible to the structural changes in the pork industry.

Chapter Four summarizes the theory and application of demand system analysis.
Three earlier estimated demand models are examined for their capability of explaining
pork demand and the model that best estimates the pork demand is used for further
analysis. Regional differences in pork consumption are also estimated based on
demographic characteristics and disposable per capita incomes.

Chapter Five addresses the issues in the supply side of the pork industry. Detailed
analysis of the cost of feeder-to-finishing Operations is carried out. The main goal of this
chapter is to analyze the competitive positions of different states/regions in pork
production and processing. This analysis is the focal point of this study because the future
of pork operations in one location lies on its cost competitiveness relative to operations in

other locations.

11

Chapter Six evaluates the regulatory pressures that the pork industry is facing.
State and federal environmental regulations are summarized and each state is assigned
with an environmental stringency index. All the states are then classified into five
different stringency groups based on their stringency indices. The U.S. Environment
Protection Agency (EPA) proposed technology options for manure management.
Tentative compliance costs attached with stringency indices and the technology options
are assigned to each state. The compliance costs then are linked to the enterprise budgets
developed in Chapter Five. Chapter Seven is the application of mathematical
programming method to find the optimal locations of pig feeding operations and pork
processing plants. This chapter utilizes all the components discussed in previous chapters
that contribute to shape the pork industry. A linear programming approach is used to
minimize the total costs of production, processing, and transportation under several
constraints. Chapter Eight consists of results and discussion, and the sensitivity analyses
of the results. Finally, summary and conclusions, and the limitations of the study are

given in Chapter Nine.

C_hapt_er.l

11. Literature Review

This section summarizes and discusses literature on structural changes in the agricultural
sector in general and the pork industry in particular. The concept of market coordination
system is introduced and the prevailing market coordination in the hog industry is
discussed. This chapter gives insight into the nature and process of structural changes. A
flow chart that depicts the concept and a process of spatial shift induced by technological
change is discussed at the end this chapter. Since this dissertation analyzes various
aspects (e.g. supply, demand, and government regulations) of hog industry, relevant

literature is reviewed and cited beyond this chapter.

2.1 Industry structure

A growing economy is characterized by a decline in relative contribution of the
agricultural sector. Slower rise in demand for food as compared to other goods and
services contributes to this process. Rapid development of new farm technologies leads to
expansion of food production per unit land and labor (Johnson, 1995). Technological
developments increase total output per unit of land, but farm profitability may not
increase due to lower prices received by farmers, which puts pressure on the agricultural
sector.

Industrialization of the U.S. ag-economy is transforming farming from self-
sufficient enterprises, to specialized and interdependent firms. The number of farms in
the U.S. peaked during the Great Depression and has decreased ever since particularly

during the 19705 and 19805. The decrease in farm numbers exceeded 70 percent from

 

1969 to 1992 (McBride, 1997). One widely held view of the future of American
agriculture is that it will continue the current trend toward fewer but larger farms, greater
centralization, and vertical coordination (Stauber, 1994). Historically, the decline in
number of farms is most prominent in the livestock sector.

The role of information and knowledge in the industrialization of the pork sector
is important for business success. People with unique and accurate information and
knowledge have increasing power and control of the sector. The capacity to capture
profits and transfer risk comes from power and control (Boehlje and Schrader, 1998).
The structure plays an important role in the process of transformation of information and
knowledge among industry participants.

Industry structure can be defined in different ways. It may refer to the
distribution of sales, revenues and profits; the importance of farm income; concentration
of production in different regions; degree of specialization; ownership and control of
inputs and outputs; and number and size of firms (Offutt et al., 1997).

Martin and Norris (1998) emphasize three different factors to describe the
industry structure. These factors are: size of operation (number of head or acres of land);
form of vertical coordination (coordinating mechanism spectrum ranging from spot
market to complete ownership integration); and location of Operations (shifts of animal
production between regions and clustering of production within a region).

The U.S. pork industry has experienced dramatic restructuring during the 19805
and 19905. It is undergoing increased consolidation of production units (decreased in
number of farms but increased in the number of animals per farm), change in location of

production within or between regions and change in coordination mechanisms. This

14

restructuring is referred as industrialization of the pork industry. A variety of forces such
as government intervention through policies designed to promote new technologies, and
favorable business climates that allow entrepreneurs to combine low cost production with
minimal regulation are cited as catalysts that cause industries to undergo rapid regional
expansion (Gillespie et al., 1998). These factors vary among regions, states, and different
counties, and therefore, influence industry structure.

2.1.2 Historical perspectives

The United States is one of the major pork producing countries in the world and
its production accounts for 10 percent of the total world supply. The U.S. was the largest
exporter of pork in 1997 followed by Denmark and Canada. The pork industry in the
United States is an important sector of the economy. Over 17 billion pounds of pork
were processed from about 92 million hogs in 1997 (USDA, 1997).

Hogs have been considered as a means to add value to corn. Therefore, the U.S.
pork industry has been historically centered in Corn Belt states (Iowa, Ohio, Indiana,
Illinois and Missouri), since corn is the primary input for hog production. These states
contributed 72 percent Of total hogs marketed in 1995 in U.S. However many pork
operations have begun to move to new locations (toward the South) such as North
Carolina and Oklahoma and the size of the operations is getting larger. The South’s share
of the national swine inventory rose from 15.8 percent in 1989 to 26.7 percent in 1996.
Martin and Zering (1997) have described the process as following:

“Fueled by technological change and economic opportunity,

the historic patterns of geographic location, farm size,

packing plant size, and organization of pork production are

15

 

changing at exceptional rates in the United States and in the

South. The number of swine farms keeps falling, with the

majority of those exiting the industry keeping fewer than

1,000 head in inventory. In contrast, total inventory of farms

with at least 2,000 head in inventory is growing rapidly.”

The production and pork processing operations are not only moving, but also are
departing from the small farm toward large and integrated operations. The output of the
20 largest packers represented 86.5 percent of 1993 hog slaughter and the 45 largest
producers, each marketing more than 62,000 head in 1993, represented 13 percent of the
total U.S. hog production (Lawrence et al., 1997). The share of total hog numbers held
by large operations with 2,000 or more head went from 33 percent in 1993 to 37 percent
in 1994 (Southard, 1995). According to recent data, 55 percent of all hogs were produced
on farms with more than 2,000 animals, and 35 percent of all hogs were on farms with
5,000 or more hogs (Seidl, 1999).

From 1969 to 1992, the number of farms selling hogs decreased by 70 percent but
average sales (undeflated) per farrrr increased by 300 percent during the period. The
change in geographic distribution of pork operations during this period was also
dramatic. North Carolina ranked eleventh in 1969 in hog production and it moved up to
second place in 1992 surpassing the major hog producing states (Illinois, Minnesota,
Indiana, Nebraska and Missouri). Changes in geographic concentration of production
between 1969 and 1992 resulted in a decrease in number Of pork producing counties in
Iowa, Illinois and Indiana. Only a few counties in these states account for 25 to 50

percent of total sales. More counties from non-traditional hog production areas, primarily

l6

1.... 1". “L"! A

North Carolina, Arkansas, Colorado, and California became part of the most concentrated
areas of hog production (McBride, 1997).

One can ask, why do these dramatic changes in production, processing and
marketing in the pork sector exist? Martin and Zering (1997) argue that the technological
improvements have led to economics of scale in production. Furthermore, improved
housing facilities, disease control measures, advances in nutrition, feeding regimes, and
animal breeding have allowed large-scale, specialized pork production to prosper.

2.1.3 Vertical coordination system

Vertical coordination is the alignment of direction and control across segments of
the production/marketing system (King, 1992). Firms enter into vertically coordinated
relationships for several reasons: to increase efficiency, gain market advantage, reduce
uncertainty and obtain or reduce the cost of financing (Mighell and Jones, 1963).
Processors participate in vertical arrangements to assure the continuous supply of
products with particular characteristics. Similarly, input suppliers also participate in
these relationships to transfer/protect their technologies (F eatherstone and Sherrick,
1992). The coordination can be achieved through direct market transactions and/or
vertical integration (direct acquisition/ownership). The coordination system can be
discussed in the following continuum based on the degree of coordination. The
continuum ranges from the loosely coordinated spot market to the tightly coordinated

vertical integration system.

2.1.3.1 Spot market

Spot market, also known as cash market, provides the exchange of commodities
or financial instruments for immediate delivery. Prices and external control mechanisms
are major factors for the coordination between the actors of economic exchange
relationships. Spot markets are open, impersonal, and do not have contractual
arrangements. These markets encounter difficulty in conveying the full message
concerning attributes (quantity, quality, timing, etc.) of a product and characteristics of a
transaction (Boehlje and Schrader, 1998). Coordination between the actors is achieved
through the control mechanism that comes externally (from market forces) but sometimes
an actor with market power can influence the market and specify some terms of
exchange. Weaker actors who cannot influence the market can reserve the right to walk
away from the exchange.
2.1.3.2 Contracts

Contracts are legally enforceable arrangements between individuals and/or firms
involved in the transfer of goods and services. Economists have recognized the
importance of risk in business arrangements. Kliebenstein and Lawrence (1995) argue
that the primary reason for contractual arrangements (e. g. marketing contracts) in the hog
industry is risk management. Production contracts help to reduce income risk and,
therefore, contracts are generally helpful to risk-averse producers. New operators tend to
have lower net worth and one might expect them to be more risk averse than existing

producers (Gillespie et al., 1996).

2.1.3.3 Strategic alliances

The coordinating mechanism in which the parties involved in exchange
relationships come together with mutual agreements. The coordination comes from
common identifiable objectives, mutual control and decision-making processes, and
sharing of risks and benefits. A breach of expectations by either party may terminate the
alliance and it does not need legal or third party enforcement.
2.1.3.4 Formal cooperation

In the cooperation scheme, there is formal organization with distinct identity and
internal control. Joint ventures, partial ownership relationships, clans, and other
organizational forms requiring some level of equity commitment between the business
partners form the formal cooperative arrangements (Wysocki, 1998). The control is
decentralized among the parties and the ownership. Actors in this relationship maintain
their identity and are able to walk away from the relationship if they wish. Agricultural
cooperatives in the US are examples of formal cooperation.
2.1.3.5 Vertical integration

Vertical integration relies on centralized control to achieve coordination.
Business decisions are made centrally which controls the operations. Single ownership
may not result in vertical integration and generally vertical integration has multiple
ownership structure. Lesser transaction costs than market exchange has become the

conventional wisdom for vertical integration as suggested by Williamson (1979).

2.1.4 Coordination in pork sector

Production contracts in the pork industry are common mainly in areas of rapid
expansion of operations. Producers provide labor, utilities and physical facilities and the
contractor provides feed, pigs, veterinary care and market hogs after finishing. The
contractor bears risks and keeps residual profits and losses. Contracts and vertical
integration are important in obtaining consistent supplies of high quality pork.
Coordination in production and marketing can improve the quality of hogs slaughtered
and reduce the transactions costs. Genetics and weight determine the value of hogs
received by packers. Use of long-term contracts reduces the sorting, measurement,
grading and monitoring costs. In fiscal year 1996, Smithfield Foods Inc. obtained
approximately 61% of its hogs through long-term agreements and integrated operations
(Martinez et al., 1998).

From the early 19805, hog production under contract became more widespread,
mainly in the Southeast where larger companies followed the integrated broiler
production model. Contracting is also growing in the Midwest, but this production
arrangement is relatively new.

Asset specificity2 in the production process is another incentive for contracts and
vertical integration. Specific assets generate quasi-rent3 streams because these assets are
hard to substitute and rents are appropriated through opportunistic behaviors. Martinez et
al. (1998) suggest that the long-term contracts and vertical integration may be helpful in
reducing the potential for opportunism in the development of pork products with unique

quality characteristics. If the packer lowers the premium, producers are left with the

 

2. Specific assets are assets whose value is much greater in particular use compared to the next best
alternative use.

alternative of accepting the premium or selling their product in spot market for no
premiums. Legally enforceable long-term contracts provide protection against short-terrn
opportunism.

2.1.5 Technology induced structural change

Methods of production improve over time. Development of new techniques,
equipment, medicines and feeds make it feasible to handle more animals in one location
than ever before. These new developments can be viewed as substitution of capital for
labor. It is important to capture these improvements in the production process to be more
profitable in business. Technological change is one of the driving forces for structural
change (Reimund et al., 1981, Gillespie et al., 1997). The structural change model for
agricultural sub-sectors has four dynamic stages.

1. Technological change

2. Shift in location of production

3. Growth and development and

4. Risk and transaction cost adjustment.

Advances in mechanical and engineering technologies has provided better
housing environments for growing pigs. Continuous improvements in feeding and
cleaning equipment increase labor and feed efficiencies. Similarly, advances in animal
breeding, nutrition, and disease control are taking place continuously. Development in
engineering and biological technologies have reduced the amount of time and feed
required for raising pigs and has also reduced mortality. These technological changes
have the following sequential structural impacts in the pork industry (Reimund et al.,

1981y

 

3 . Value of the assets in excess of the salvage value.

21

' The technology is employed by large producers and early adopters

' Requirement of capital increase to adopt new technology

I Land and labor productivity increase

' Development of economies of size

I Value Of resources increases

' Shift in location of production

Fig. 2.1 outlines the process of the technology-led structural change model. A
shift in production location brings new producers, and other resources into the sub-sector.
Firms look for the production sites that have lower input prices to increase their net return
to the investment. Production may be concentrated in the sites where pork production
turns more profitable and hence the new technology is adopted in new areas. Innovative
industries experience rapid grth and development.
Ex-post spot market introduces the opportunistic behaviors of market participants

(e.g. meat packers and the producers). Ex-ante marketing arrangements prevent them
from such behaviors. Larger and specialized farms produce a large volume of production
and have less product diversification. Such farms may need to sell their products at a
lower price in spot market if they did not have formal marketing arrangements before the
production process. Due to productivity growth and specialization, industries become
more risky (price risk). Recent trends of increasing the size of farms and rapid
technological development results in increased risk in production and marketing.
Production and marketing chains become more tightly coordinated to minimize the risk
due to over-production and less diversification. Ex-ante contracts and vertical integration

minimize the risk in the production marketing chain. Since this research is particularly

IQ
IQ

interested in the shift in production location, changes in the industry beyond the shift in
production locations will be only briefly discussed in this research project.

Figure 2.1: Conceptual structural change model

Technological Shift Growth & Risk
Change > Production > Development A Adjustment

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Early Adaptors New Resources Larger Farms New Risk
F, a (New Tech) '—" Aversion
v7 vb < 7 U7
More Capital New Production Specialization New
Required AI“ 3"" . Coordination
Concentration
«V7 {“7 M {“7
Increase Spatial Increased Contracts &
Productivity Concentration Output Forward sales
< r < r <Vr V“;
Increase _i New Tech in Over Production Increased
Resource Value New Area Market risk Coordination

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source: Adapted from Reimond, Martin and Moore (1981) with modifications.
Description of the conceptual model

The meat packing industry in the U.S. has developed over the last 150 years in
response to technological changes. In the process of development, the industry has moved
from small, local butchers to central terminal stockyards to slaughterhouses. Large-scale
hog production using the new technologies has increased in the southern and western
regions. New operations are typically linked with a packer and or feed mill by contract
or joint ownership agreement to reduce risk and transaction costs (Gillespie et al., 1996).
A marketing structure with fewer, large-volume buying stations would be more

operationally efficient.

23

The hog industry has passed through several vertical integration and horizontal
merger waves in the past. Williamson (1989), Perry (1989) and Katz (1989) discuss the
benefit and motivation for tighter vertical integration. Williamson argued that the main
purpose of vertical integration is to minimize transaction costs. Perry has suggested
transaction costs, technological changes, and market imperfection as major causes
leading to vertical integration. Vertical integration can also spread the risks involved in
production and marketing.

2.1.6 Government regulations and pork industry

In addition to labor force characteristics, input availability, and proximity to final
markets, government intervention on different levels and issues can also influence the
location of firms. Public policy tools can encourage or discourage current or future
behaviors. Grants and subsidies from government provide incentives whereas regulations,
standards and moratoria are disincentives (Seidl and Grannis, 1998). Similarly, taxes,
zoning, quotas, permits, research and education are some public policy tools that play
important roles in industry structure. Animal feeding Operations generate manures that
are rich in mineral nutrients such as nitrogen and phosphorus. Such nutrients are useful
to the plants, but are detrimental to human health if the air and drinking water are
contaminated. The loading of nutrients into water and air, as the impact of large hog
feeding operations has generated debate in several state legislatures. The National
Survey of Animal Confinement Policy (1998) under the auspices of the National Policy
Education Committee has covered various aspects of government regulations. The
regulations relevant to pork industry are analyzed and discussed in a separate section of

this dissertation.

24

Chapter 3

III. Pork industry: trends in production and firm locations

The pork industry is reorganizing to meet the changing expectations Of consumers. The
industry has built new alliances with breeders, producers and processors to ensure
superior pork products. The consequence of these arrangements is the development of a
supply chain structure. Breimyer (1962) noted that crop, livestock, and marketing were
three distinct economies in the U.S. agriculture and the livestock industry was an
intermediate stage between a traditional agrarian structure and a modern industrialized
model. The relative emphasis of crop and livestock within major production regions
changed due to the industrialization of agriculture. In this chapter, the supply situation of
pork with emphasis on historical trends is discussed. The spatial distribution of pork
production is of primary interest in this research; therefore, the geographical

concentration of hog production is analyzed.

3.1 Trend in number of hog farms and production in the U.S.

Since the beginning of the 20th century, the number of pig farms in the United
States has been falling, but the average inventory per farm has risen steadily. In past
decades, the number of farms, particularly the smaller farms (i.e., less than 100 head in
inventory) has dropped sharply, but the number of farms keeping more than 1,000 head in
inventory increased until 1992 and the number dropped slightly in 1997. The drop in the
number of big farmers from 1992 to 1997 is the result of consolidation of bigger farms
into larger operations. The data presented in Table 3.1 are helpful to explain the

structural changes in the U.S. pork sector in terms of number and size of farms.

25

Table 3.1 Trends in number and size of hog farms (1959-1997)

 

Number of hogs sold per farm (in thousands)

 

 

 

 

 

 

 

 

 

Census Total farms 1-99 100- 1 99 200-499 500-999 1,000+
Year (in thousands)

1959 1,273 1,018 161 81 10 1.5
1969 604 361 109 101 25 6.6
1978 470 281 69 74 30 15.8
1982 315 162 44 56 30 21.6
1987 239 110 33 45 27.5 23.9
1992 188 77 23 35 25 27.8
1997 110 43.7 9.6 15 12 21.7

 

 

 

 

 

 

 

Source: U.S. Census of Agriculture

evenly distributed. Historically, pork production has been concentrated in the Corn Belt
states in the North-central region. Iowa ranked number one in the nation in hog numbers
with 26 percent of the nation's supply (Melvin, 1996). According to the 1999 December

data, Iowa’s share decreased to 24.6 percent, but still ranked number one in the nation in

Pork production operations are found in every state in the nation, but are not

terms of total hog numbers. Production units in the 200 to 499 head of annual sales

declined in 19705. Similarly, production units in the 500 to 999 head of annual sales
declined in 19805. In 1978, the U.S. Census showed one-third Of output produced by
units marketing 1,000 head or more per year, but only seven percent by those large units

marketing 5,000 head or more. In 1992, 1,000 head group marketed 69 percent and 5,000

head group was marketed at 28 percent (Rhodes, 1995).

26

 

Figure 3.1 The United States of America and geographical regions

 

 

 

 

 

In addition to size change, pork-producing operations locations have also been
changing in the past 25 years. The North-central region remains a major production area,
both in number of operations and number of animals, but there has been continuous
growth in pork production in the Southeastern and Western Corn Belt regions4 (Table
3.2). Table 3.2 shows the position of major pork producing states in terms of number of
operations and hog inventory in different years. From 1978 to 1997, the number of hog
Operations decreased by 75 percent and the total number of hogs increased by six percent
in the nation. The increase in the number of hogs during this period is dramatic in North
Carolina (406 percent increase), but the number of operations decreased by 84 percent.

The total number of hogs in Iowa remained nearly constant, but the number of hog farms

 

4 According to Bureau of Economic Analysis (1997) grouping of states in region
Northeast: ME, NH, VT, MA, R1, CT, NY, NJ, PA
Midwest (Eastern and Western Corn Belts): OH, 1N, IL, Ml, Wl, MN, 10, MO, ND, SD, NE, KS
South: DE, MD, VA, WV, NC, SC. GA, FL, KY, TN, AL, MS, AR. LA, OK, TX
West: MT, ID, WY, CO, NM, AZ, UT, NV, WA, OR, CA, AL, H1 (Fig. 3.1)

27

decreased by 70 percent. Illinois and Indiana experienced declining trends in both hog
operations and inventories. In Montana, the number of hogs has decreased by 16 percent
and the number of operations has decreased by 74 percent during the period. Similarly,
in Michigan, the number of farms decreased by 67 percent and the number of hogs
increased by 11 percent during the period. From this table we may conclude that the
decrease in number of hog operations is drastic in most of the cases. Inventories of hogs
have been increased dramatically in a few states and in most of the states it is either
constant or it has decreased slightly.

Hog production is concentrated among the top five producing states (Iowa, North
Carolina, Minnesota, Illinois, and Indiana). In 1997, these five states supplied about 70
percent of the total production. Iowa was the largest hog producing state, representing 24
percent of the U.S. hog inventory in 1997. The second largest producing state was North
Carolina with about 16 percent of inventory. Despite North Carolina’s large production
share, the majority of commercial hog operations are still located in the Midwest, the
traditional hog producing area. In 1997, Iowa had the most hog operations with 17,243.
Other states with large numbers of hog operations included Minnesota (7,512), Illinois

(7,168), Indiana (6,442) and Nebraska (6,017 operations).

28

Table 3.2 Number of operations5 and hog inventory in selected states (1978-1997)

 

 

 

 

 

 

 

 

 

State Number of Operations Number of hogs (Thousands)
1997 1987 1978 1997 1987 1978
Iowa 17,243 36,670 57,325 14,652 12,983 14,695
N. Carolina 2,986 6,900 18,846 9,624 2,547 1,901
Minnesota 7,512 16,042 25,703 5,722 4,372 4,089
Illinois 7,168 17,084 28,227 4,679 5,642 6,206
Indiana 6,442 14,834 22,141 3,972 4,372 4,160
Nebraska 6,017 13 ,3 63 20,532 3,452 3,944 3 ,723
Michigan 2,853 5,577 8,572 1,032 1,227 931

 

 

 

 

 

 

 

 

 

Source: U.S. Census of Agriculture 1982, 1987, and 1997

Historically, hogs have been raised on farms that produced corn and other crops.
However, in the past three recent decades, farming has become more specialized. The
size of production operation is growing rapidly and many small to mid-size farmers have
abandoned raising hogs. The number of farms that sold hogs was 645,882 in 1969. The
number reduced to 312,924 in 1982. This number was further reduced to 138, 690 in
1997 (Table 3.3). The share of hog slaughter rose from 34 percent in the top four firms in

1980 to 56 percent in 1998 (Carstensen, 2001 ).

 

5 The definition of a farm for census purposes was first established in 1850. It has been changed nine times
since. The current definition, first used for the 1974 census, is any place from which $1,000 or more of
agricultural products were produced and sold, or normally would have been sold, during the census year.
The farm definition used for each US territory varies. The report for each territory includes a discussion of
its farm definition.

29

Table 3.3 Number of farms and per farm pig production 1969-97

 

 

 

 

 

 

 

 

Year Farms Live wt. Head per farm
(Sale hogs) (Mil. Pounds) (Average)

1969 644,882 20,600,325 138

1974 449,266 19,976,384 177

1978 422,873 19,466,200 214

1982 312,924 19,657,921 300

1987 236,973 20,408,228 403

1992 186,627 23,946,691 588

1997 13 8,690 24,094,229 1491

 

 

 

 

 

 

Source: Compiled from the U.S. Census of Agriculture Data

The number of farms with hog sales declined by about 78 percent between 1969
and 1997, but the total hog production increased by about 17 percent. The average
number of hogs sold per farm jumped from 138 to 1491, which is over a ten-fold increase
from 1969 to 1997 (Table 3.3). The increasing trend of production and decreasing trend
of the number of farms can be represented from the following chart. The chart shows
annual production of hogs in terms of total live weight and the number of farms for

census years from 1969 to 1997.

30

Figure 3.2: Trends in pork production and number of pig farms in the U.S.

 

 

 

 

 

 

 

 

 

 

715,000 r I» 30000
; 615.000 ' i
i a i Production 25000 i
; E 515,000Jr 1 ,, .
a 1 l u l
i 1; 415’000I 31-20000 g i
i . i O
i I- "
} é 315,000 7— 15000 g g
1 215 000 4 i i
. 3 1 . 1
z i F ‘T’ 10000
115,000 + arms \ 1
15,000 J; . +4 £ 5000 1

 

69 74 78 82 87 92 97 1‘

i
i
5 Year
1

 

Source: Economic Research Service, U.S. Department of Agriculture

3.2 Increasing geographic concentration of production

Concentration6 in hog industry refers to the inequality in the pork production
among different geographic regions, states, and counties. Hog production was previously
concentrated in the Corn Belt area since corn is the major feed grain for hogs. However,
in the recent past decades, the pork industry has moved to new locations. Hog production
has shifted from small, geographically dispersed operations to fewer, larger, and
geographically concentrated operations. Further concentration of ownership and control is

under way in the industry (Abdalla et al., 1995).

 

6 Concentration is defined as an increased proportion of production controlled by fewer firms.

31

Figure 3.3: Distribution of hogs in contiguous U.S. 1982 and 1997

 

 

Hog Inventory: 1982

 

 

 

 

 

 

ya. on i
: .' 5
. . . v I
I. "a ' ’0.” :
. ...- . : s. ‘I ..v
1”: no.2‘ . " a '
, . a. - r" s
o. g :0" . 3' . w o .
, 5‘ 3: .3, - 223' ref. -5
l' t -
.'. ° F" ' ' "
‘_ 'I.
’

 

 

 

 

 

 

 

I: '- ° ”'35:": . One Dot=5UDUU

Source: 1982 Census of Agriculture

 

 

 

 

Hog lnverttoryzl 997

i a

., . g
”a

m?”

 

435'“.

One Dot-50.000
Source: 1997 Census of Agriculture

 

32

 

Table 3.2 and Fig. 3.3 show how the hogs are concentrated in fewer states. The states of
Iowa, Minnesota, Illinois and Indiana (the traditional hog producing states) are still
dominant in hog production. Fig. 3.3 also indicates the growth in hog production in
South (vicinity of the state of North Carolina) and the West (e.g. the state of Colorado,
Utah, and Arizona) from 1982 to 1997. The concentration in the state of North Carolina
is remarkably higher.

As discussed earlier, production of hogs is concentrated in fewer Operations. The
number of hog operations in some states reduced dramatically in 1997 and the pork
production is dominated by a fewer operations in limited areas. Most of the hog
operations that have more than 200 hogs in their inventory are still located in traditional
hog producing states. Few new emerging states such as North Carolina, Pennsylvania,
Missouri, Oklahoma and Arkansas also have high concentration of big Operations (Fig.
3.4 and Fig. 3.5).

There has been a major growth in pork production in the Southeast, particularly in
North Carolina over time. In some counties, pork production has increased dramatically.
Figure 3.5 depicts the concentration of hogs in U.S. counties. Out of the top 25 counties,
11 counties are from Iowa and eight counties are from North Carolina. This gives some
idea of how the hog production is concentrated in these two states. Texas County in
Oklahoma and Sullivan County in Missouri have seen a dramatic jump in production.
These two counties jumped from 797 and 736 ranking in 1992 to the number three and

number six top producers respectively in 1997.

33

 

Figure 3.4: Farms with 200 or more hogs and pigs inventory in 1997

 

 

 

 

Source: 1997 Census of Agriculture

 

 

 

 

Figure 3.5: Hogs and pigs sold by counties in 1997

 

 

Hon-MW

 

>35.000

Source:1997 Census of ‘_
Aoricdtue

 

 

 

34

 

3.3 Factors affecting location of production

What factors make some locations desirable for hog production over other
locations? Factors that influence the location decisions and regional shifts contribute to
the geographic concentration of hog production. Production restrictions and feed costs
are important factors for industry location. Competitiveness in state regulations for farms
and agribusiness, taxes, labor costs and characteristics, and closeness to final markets are
also the important factors (Gillespie, 1996). Some Of the factors, which potentially
influence the pork industry structure, are discussed below.
3.3.1 Technological change

There is considerable agreement among agricultural economists that the structural
change is driven by technology and efforts by producers to gain economies of scale. New
technologies and managerial techniques bring profit opportunities. The cost-saving
motivations in production processes are important factors for development and adoption
of new technologies. For example, new technologies in animal feeding have helped
reduce the amount of corn required per hundredweight of pork produced. Transportation
cost of corn out Of the Midwest has become lower over the past few years because of
volume discounts given to large producers (Good, 1994). Profit maximization and
production and distribution cost minimization are the primary factors in determining the
location (Healy and Ilbery, 1990). Technological break-through in animal health (good
nutrition and medication), all-in/ all-out production, and multi-site production have made
it possible to reduce the outbreak and spread of diseases even with very large number of
hogs confined in one location. This can be taken as an example of technological changes

contributing to industrialization of hog industry.

35

3.3.2 Corporate farming laws

Restrictive laws potentially push pork production away from particular areas
toward others (Welsh, 1998). Nine states (Iowa, Kansas, Minnesota, Missouri, Nebraska,
North Dakota, Oklahoma, South Dakota, and Wisconsin) have anti-corporate farming
laws (Hamilton, 1995 and Knoeber, 1997). The anti-corporate farming laws prohibit
corporations from owning farmland or from conducting farrn operations. The intention
of such laws is to protect the family farms by excluding agribusiness and conglomerates
from direct production and from controlling farm production (Krause, 1983).

The states of North Carolina, Arkansas. Utah, and Colorado have experienced
substantial increases in pork production. Growths in production in these locations can be
partially attributed to favorable corporate farming and environmental policies that allow
large-scale farming using non-traditional business arrangements (Gillespie, 1996). Anti-
corporate farming laws have restricted innovative corporate swine producers in the
southeast from expanding their operations to major swine producing states in the
Midwest (Knoeber, 1997).

3.3.3 Property values

Agricultural land values in proximity to hog operations may rise due to demand
for manure application rights. If there is little or no hog production in the area initially,
property values are reduced more by the addition of a hog operation (Hubbel and Welsh,
. 1998). Hubbel and Welsh suggested “ property values may push hog production into
counties where it already exists at substantial levels, because the marginal reduction in
their property values will be less in these counties”. The value of agricultural land is high

in the eastern part of the country and the west coast. Parts of New Mexico, Arizona,

36

Texas, Nevada, Wyoming, Montana, South Dakota and Nebraska have cheaper
agricultural land. These areas may interest hog producers in moving their hog production
in the future.

It may be possible that the introduction of hog production in an area of low
economic activities would increase the property value because the industry generates new
economic opportunities in the area and demand for land use would increase in order to
spread the manure generated by the hog industry.

3.3.4 Economic options

Agriculture may be one industry that will provide increasing economic benefits to
rural America through value-added agricultural practices. We can take the case of recent
changes in the southern economy. Hog production in the southern region is increasing
and it may be due to the lack of economically viable altematives for farmers. Martin and
Zering (1997) argued, “Pork production in the South was not an economically important
commodity prior to the 19705. The political climate surrounding traditional cash crops
left many farmers uncertain as to whether there was a profitable future with these
commodities. Given the small farm size and low yielding soils, individuals recognized
the need to search for and develop alternative farm enterprises”. Choice of pork
production enterprises is the result of fewer economic alternatives for the farmers in the
South. Pork production and processing enterprises have contributed economic benefits to

the communities in the forms of employment, farm income, and tax revenues.

37

 

3.3.5 Environmental adsorptive capacity

Environmental characteristics such as soil type and climate of a specific region
are important in making location decisions (Boehlj e, 1995). As the number of hogs per
unit land increases beyond a limit, the by-product may exceed the environmental
adsorptive capacity or the carrying capacity. This leads to serious environmental
problems such as high nutrient content in soil and water. The adsorptive capacity is site
specific and it is the least mobile resource. Therefore, adsorptive capacity is an important
determinant in the location of hog operations.
3.3.6 Public policies

Public policies influence technological progress. For example, the U.S.
govemment’s decision to privatize commercial production of nitrogen fertilizer during
World War II enabled rapid expansion of the use of fertilizers. Policies such as the
federal commodity price support program, Commodity Credit Corporation’s storage
program for feed grains, and improved transportation played important roles in affecting
the spatial distribution of crop and livestock production (Abdalla et al., 1995). Change in
public policy could provide a basis for the structural change indirectly through impacts
on adoption of technology, producer risks, and geographic location (Reimund et al.,
1981)
3.3.7 Consumer demand

The role of consumer demand on structural change of the hog industry is under
debate. Some economists believe that the main push for the change has come from the
demand side. Boehlje and Schrader (1998), and Barkema and Cook (1993) recently

argued that consumer driven forces are primarily responsible for the changes in the U.S.

38

pork industry. New market channels of communication such as production contracts and
vertical integration connect to consumers. Demand for good quality pork has been the
driving force behind the structural change. Consumers demand meat products with more
specific traits such as leanness, tenderness, flavor, convenience, and nutritional value.
Meat packers convey the consumer demand information to producers through production
and marketing contracts. Rhodes (1995) does not agree with these views and he argues
that changes in the hog industry are driven by profit motives. Producers expand
horizontally to control production costs and increase their returns. Location adjacent to
final markets is an important factor for production location decisions. We can take the
examples of North Carolina and Utah. North Caroline is well situated to furnish the
Eastern Seaboard with pork and Utah is well positioned to fulfill the California markets
and Asian export markets.
3.3.8 Contractual arrangements

A tightly vertically coordinated system facilitates signaling consumer preferences
back to producers. Production contracts, for example, are effective in transferring
consumer preferences. Such contractual arrangements also assure the supply of quality
hogs to the pork processing plants. Contract production enables the large processors to
continue growing rapidly. In contract production, the producer’s capital is not tied up in
building and equipment. The producer is able to direct his resources to building'more
farrowing units where more hogs can be produced. Because of the long history of
contract production in the poultry industry, contracting is readily accepted in North
Carolina. There are adequate people who maintain interest in becoming part of the

production process as contract growers and finishers and financial institutions look

39

favorably on providing capital for contract production (Goods, 1994 and Hurt, 1999).
Hog production in non-traditional areas can become competitive with the traditional area
because they can realize efficiency gains through improved managerial and production
techniques and marketing contracts.
3.3.9 Agglomeration

In production economies, there are internal and external economies of scale. It is
a well-known fact that economy of scale is one of the internal factors of expansion in
production level. External economy of scale arises from “localization economies” (Roe
et al., 2002). Agglomeration implies that performance of a pork operation improves by
the easy access of industry infrastructures and services. When many related businesses
are concentrated in one location, there becomes easy availability of inputs, technical and
administrative services. Diffirsion of production and marketing information is improved
and the transaction costs are lowered due to the geographical concentration of firms
(Krugman, 1991). Among the various factors affecting the regional competitiveness of
the hog industry, consumer demand, environmental regulations and costs of production
are the most dominant factors. Furthermore, most factors discussed above have direct or
indirect effects on production costs. These three factors are discussed in detail in the

following sections of this study.

40

Chapter 4

IV. Approximation of pork demand

4.1 Introduction

Demand and supply along with other forces shape the industry structure. Boehlje and
Schrader (1998) and Martinez et al. (1997) argued that the consumer driven forces are
responsible for the changes in the U.S. pork industry. Consumers want pork and pork
products at reasonable prices. They adjust the quantity of pork demanded based on the
market prices Of pork and other substitutes. The mathematical model used in this study
will adjust the number of pigs to be produced in different locations based on the quantity
of pork demanded. Pork production (supply) and consumption (demand) are interrelated
to clear the market. If the price falls, quantity demanded increases; if the price increases,
quantity supplied increases. The conditional predictions are combined to generate a
regional allocation in U.S. pork system.

Consumer demand for a commodity is an important component in analyzing and
forecasting the effects of changes in prices of commodities and consumer income. This
research is designed to study the consumer demand system of pork to achieve a better
understanding of its effect on the location of pork production and processing. Grannis
and Seidl (1998) argued that a change in consumer demand might be partially responsible
for the change in the hog industry. From late 19705, pork and beef lost market share to
chicken partly due to the health concerns of consumers. Decrease in market shares Of
pork and beef is partly contributed to the reduction in production costs of chicken due to

technology advances. Moschini and Meilke (1989) concluded that the movement toward

41

white meats supports the idea that dietary concerns are partly responsible for the changes
in the pattern of meat consumption. Today’s consumers prefer meat products that are
leaner, more consistent, and more convenient to prepare. In order to meet consumer
needs, pork processors build alliances with hog breeders and producers to ensure
breeding and production decisions that yield a superior quality of pork. Such alliances
are more effective if the market participants are located closer to each other. Pork
production and processing firms move to the locations where the demand for pork and
pork products is larger.

The objectives of this chapter are to give a brief description of the theory and
application of meat demand system analysis and to estimate the market demand of pork
in the U.S. This chapter will use three different meat demand models that are published
in different journals to estimate the pork demand. The log linear, the Rotterdam, and the
almost ideal demand systems will be used on per capita pork consumption data. The
model, which explains the per capita pork consumption better than the other models, will

be picked for further analyses of the pork sector.

4.2 Theoretical background

Directly specified and utility-based demand models are two general approaches of
demand analysis. Directly specified models have been used for many years and are built
on the economic theory of consumer behavior relative to price and income. Utility-based
models are built on the assumption that the consumer behaves rationally and chooses the
consumption basket so as to maximize his/her utility subject to a budget constraint.

According to Theil and Clements (1987), these demand models give an intuitive

42

explanation of the parameters of the demand equations and can be used to test the
empirical validity and theoretical restrictions of demand equations.

Economic theory tells us that consumer demand is a function of consumer
income, tastes, and the price of goods. Phlips (1974), Theil (1975), Deaton and
Muellbauer (1980), and Johnson et al. (1984) have documented the theory of consumer
demand. The utility function is a measurement of consumer satisfaction obtained from
the consumption of a bundle of commodities at a given time. The consumer as a rational
decision-maker, chooses the commodity mix so as to maximize his/her total utility.

The utility maximization problem can be written as

MaxU = f(q,,q2,q3, ........ ,q,,) (1)
Subject to Z piqi S I (2)
i=1

where, 'U’ is the consumer utility which is strictly increasing, strictly quasiconcave and
twice differentiable, p, and q, are the price and quantity Of the it" commodity. ‘1’

represents the consumer total income and it is equal to the expenditure (no savings
assumption). Equation (2) is called a consumer budget constraint and states that total
expenditure on all commodities is not greater than total income. Expression 1 and 2 can

be solved and rewritten as a Marshallian demand function (mi)

qi=mi(19pimpn) (3)
This Marshallian demand function indicates that the demand for a commodity (q,—) is a

function of its own price (p,), prices of other commodities (pn) in the consumption

43

bundle, and individual’s income (I). A utility function expressed as the function of
quantity consumed, i.e., U: f (q) is called a direct utility function, whereas a function
expressed in terms of prices of commodities and consumer income is called an indirect

utility function. The indirect utility function can be expressed by the following equation

U = f[m,(I,p1...pn),...., mn(1,p,...pn)] (4)

where m], ..... m" are the set of Marshallian demand functions from equation (3) which can
be written in a short form as

U =V(I,P) (5)

where V is an indirect utility function. Application of Roy’s identity to the indirect utility

function gives rise the quantity demanded.

aV/Op, _

aV/aI — q” (6)

The indirect utility function is useful for determining what change in income is necessary

to compensate for a given change in price and still keep the utility of consumer constant.
Similarly, a cost or expenditure function can also be used to derive consumer

demand. This approach assumes that the consumer minimizes the cost of attaining a

given utility ‘ U’ at the price ‘p’. This minimization problem can be written as
C(Uapi)=mlnqi Pi-‘Ii (7)

subject to U(q1, qz, ...... , qn) = U (8)

where, C (U, pi) represents the cost of the optimal quantities Of q,- at price p,- ( i = 1,2

n) and utility level U. Total expenditure (E) is represented by Zpg, = E, which is the

44

least expensive way of reaching the highest possible utility level. The cost minimizing
demand function tells us how the quantity consumed is affected by prices given the utility

(U) held constant.

By taking partial derivatives of the given cost function with respect to prices, we
can derive the Hicksian compensated demand function (h, ). The process is called the

Shephard’s lemma and it gives the quantity (q) demanded as below

5C(U,P.)

2}, U, .- = 1
OP, ,( P) 6] (9)

4.3 General restrictions and assumptions of demand systems

The system of demand equations from utility maximization or cost minimization has four
basic general properties that take the form of mathematical restrictions. These properties
are (1) adding up, (2) homogeneity (3) symmetry and (4) negativity7.

1. Adding up: By this restriction, the sum of the budget shares is equal to ‘one’ and
implies that the total value of all the goods in the basket is equal to total expenditure.

2. Homogeneity: Homogeneity implies that the Marshallian demand functions are
homogeneous to degree zero in prices and income. Quantity demanded remains
unchanged if all the prices and income changes are by the same proportion. Hicksian
demand are homogenous to degree zero in prices.

3. Symmetry: The cross price derivatives of the Hicksian demands are symmetric for all

i¢j and this symmetry condition can be represented by

 

7 For a detailed discussion, refer to Theil (1975), Deaton and Muellbauer (1980) and Phlips (1983). The
demand systems summarized here are based on these publications.

45

6’C(U,p) : 62601.12)
617in apjpi

 

 

(10)

where, C(U,p) is cost function and the Hicksian demands are obtained by taking
derivatives of this function as described in equation (9). According to Young’s theorem,
if these two derivatives are continuous, they are identical and the order of differentiation
doesn’t matter. The symmetry property guarantees that consumers make rational and
consistent choice.

4. Negativity: This negativity condition implies that an increase in price with utility held
constant must cause demand for that good to fall or at least remain unchanged.

62C(U,p)
6,021

 

so (11)

Adding up and homogeneity conditions are consequences of the specification of budget
constraint. Symmetry and negativity derive from the existence of consistent preferences.
Phlips (1983) has explained that observed consumer behaviors Often do not satisfy the
theoretical restrictions because theory is a simplification of reality and statistical data
generally contain some measurement errors. It is not possible to include all the items
entering into the consumer’s budget in demand system analysis because the number of
parameters to be estimated becomes very large. For example: for a system of “n” items,
we need to estimate n (n+1) parameters. By application aggregation and symmetry
restrictions, the numbers of parameters to be estimated are reduced to ‘/2 (n2 +n)-1.
Degrees of freedom and multicolinearity are important econometric problems of using

large systems.

46

The consumption bundle is partitioned into subsets each including items that are
substitutes or complements to each other than to items in other subsets. The concept of
separability is useful in the two-stage budgeting procedure for the allocation of the
consumer’s expenses across the group of goods. First the consumer allocates his/her total
expenditure to broad commodities groups such as food, housing, clothing, recreation etc.
Then he/she optimally allocates spending in specific goods (e. g. pork) in a particular
group (e.g. meat). Application of separability assumption makes demand analysis

simpler.

4.3 Market demand

An individual consumer or household is the basic unit of demand analysis. The market
demand for a consumer good is the sum of the consumers’ demands. Economists
generally use two approaches to estimate the market demand system. The first approach
specifies functional forms to estimate the demand parameters. It incorporates the
separability assumptions, which have the advantage of reducing the dimension of the
estimation problem and imposing behavioral restrictions. With this assumption, the result
of the demand theory for individuals can be evaluated in market level data (Johnson, et
al., 1986). Brandow (1961) and Frisch (1959) pioneered a second approach that deals
with the approximations of demand systems. This approach is common in the discipline

of applied economics and used in policy and commodity market analyses.

47

4.4 Demand model specification

There are four basic approaches to the derivation of the theoretical demand system.
1. Linear Expenditure system (LES)

Klein and Rubin (1947-48) developed LES model. This approach maximizes
utility function subject to the budget constraints. They expressed the expenditure on a
good as a linear firnction of total expenditure and all prices. They imposed the adding up,
the homogeneity and symmetry restrictions in the system. Stone (1954) applied the LES
in Britain. This approach was popular until the 19705 and is still in use.
2. Indirect utility function approach

The indirect utility function approach is based on the algebraic specification of the
indirect utility function. The optimum quantity demanded depends indirectly on the
prices of goods being bought and the individual’s income level so as to maximize the
utility. Roy’s theorem is used to obtain demand function from the corresponding indirect

utility function (v). The theorem can be expressed as:

_ 612/ 6p,
‘1: _—__av/61 i=1,....,n (15)
3. Marshallian demand function approach

This approach is a direct approximation of the Marshallian demand functions.
This is similar to Stone’s (1954) logarithmic demand ftmction with some variation. The
first order approximation of the demand system is used instead of logarithms. The

Rotterdam model is based on the Marshalian demand function. Rotterdam models are

specified using prices and measure of real income. Logarithmic differentials of demand

48

functions developed under this specification are expressed as
n

A(In q,)=Zy,jA(ln Pj)+,6,AlnI (,6)
1:1

where 7 ij is the cross price elasticity of the ith commodity with respect to the jth price

and ,3, is the income elasticity of the commodity ‘i’. The real income ‘I’ can be replaced

by Divisia volume index (DQ)8 as suggested by Theil and Clements (Alston and
Chalfant, 1993). Individual commodity demand functions are then weighted by their
corresponding expenditure prOportions w,
4. Cost function approach

This approach transforms the consumer’s problem from maximizing utility with
respect to prices and income to that of minimizing the cost of attaining a given level of
utility with the same prices and income. Deaton and Muellbauer (1980a) used the cost
ftmction approach to derive the Almost Ideal Demand System (AIDS) model. The AIDS

model can be represented in the budget Share form.
w,=a,+Zr,j lnpj+fliln(%) (l8)
1'

where, w, is the budget share Of good i, E is the total expenditure and p represents the

price index, and or, , B, and rjj are their parameters associated with intercept, prices of

 

. DQ=Z§.A1nq.~ (m

where, D0 = Divisia volume index, 3'1: average market share of commodity i, and q = per capita
consumption of commodity 1. D0 is used to replace the income variable in the demand equation.

49

meats and expenditure respectively. The model that uses this price index is called “Linear

Approximate AIDS (LA/AIDS)” model.

4.5 Demand system approximation and application

The AIDS and the Rotterdam model are two demand systems commonly used by
applied economists. The AIDS model is popular due to its flexibility, compatibility with
aggregation over consumers, and simplicity to estimate and interpret. Similarly, the
Rotterdam model is becoming popular and is argued to be a good alternative model to the
AIDS model (Alston and Chalfant, 1993). Both systems are consistent with the theory of
consumer demand. Alston and Chalfant compared two econometric demand systems to
explain quarterly U.S. meat demand (1 967-198 8). They concluded that the Rotterdam
model was superior to the AIDS model based on the specification test. But, the authors
have cautioned that their results should not be taken as evidence in general and other data
sets could yield opposite conclusions.

4.5.1 Pork demand approximation
The Rotterdam and the AIDS specifications by Alston and Chalfant (1993) and

log linear demand model by Hahn (1998)9 are chosen to examine how close these models

 

9.Rotterdam Model ;,A In q, = r, + 2 gap] + 2 70A In p). + fl’DQ (19)
i=1 j=l
AIDS Model: AS,- = F, +ZHU-Dj +Zyy~AlnPj +fl,DQ ' (20)
1:1 1:1
Log-linear Model: In q, = 130 + 27,) 1111’,- +flrDQ (21)

j=1
Where F and ,60 are intercepts, Dj are seasonal dummies, Pj are prices of meats, Y, is per

capita income in time t. s, =market share and q, is quantity of meat demanded and DO is
Divisia Volume Index. Description of models is given above in theoretical background
section.

50

can explain the U.S. pork demand. In order to calculate per capita pork consumption,
parameters associated with prices of meats and income were estimated by using the meat
demand models by Alston and Chalfant (1993) and Hahn (1998) with little modification.
This research is interested in predicting long-term pork demand rather than
quarterly fluctuations. Quarterly dummy variables that capture seasonal effects in the
original demand equation (equation 19) are removed. Estimating Divisia Volume Index
(DQ)10 for each state is tedious because prices of meats in each state are difficult to
Obtain, if not impossible. To overcome this problem, we assumed single DQ for all states

to estimate per capita pork consumption. The equations 19-21 now can be written as,

3113111511: 12+ ZVyAln P; + fliDQ Rotterdam Model (22)
1:1
AS: = 17+ 2 MA 111 P) + flrDQ AIDS Model (23)
j=l
In (I,- = F, + Z 7.) In P,- +181DQ Log linear model (24)
1:1
where ,

F i are intercepts, Pj are prices of meats (beef, pork, chicken and fish) , Si =share of meat

i on total meat expenditures, q, are quantity of meats demanded. Each model consists of

four simultaneous equations to estimate the per capita consumption demand (national
level) of beef, pork, chicken and fish. Model 22 estimates product of moving average of

consumption share and change in log of quantity of meat i demanded. Model 23

 

lO DQ = z 3",Aln q,

51

estimates change in expenditure shares of meats and model 24 estimates log of per capita
pork consumption. From the estimated left-hand sides of the equations, we can calculate
the per capita demands for different meats. Due to the simultaneity problem, the quantity
of four different meats demanded and their prices were included to estimate the
parameters for the pork demand. This research is interested in pork demand only.
Hereafter, only the pork demand parameters and estimation are discussed.

Three alternative demand estimation models given in equation 21 to 23 were used
to estimate the per capita pork demand in U.S. in order to determine the best econometric
model. Comparison of estimated demand by these models and observed demand are
listed in Table 4.1 and Table 4.2.

Table 4.1 Goodness of fit of the models in predicting pork demand

 

 

 

 

Model R-squared (%) Root MSE % Deviation ’ Paired T-Test2
Rotterdam 94 0.007 1.75 NS
AIDS 25 0.007 0.90 NS
Log-linear 28 0.057 5.80 **

 

 

 

 

 

 

 

1. Average deviation from observed pork demands.

2. Comparison between the observed and estimated pork demands.
Ho: mean (Rotterdam - observed) = mean (diff) = 0,
Ho: mean (AIDS - Observed) = mean (diff) = 0 and
Ho: mean (Rotterdam - AIDS) = mean (diff) = 0 are failed to reject.
Ho: mean (Rotterdam — loglinear) = mean (diff) = 0 is rejected.

Based on the goodness of fit, the Rotterdam model and the AIDS model are able

to describe the per capita pork demand more precisely. Demands estimated by the log-

linear model have higher deviations (forecasting error) from the observed values and null

hypothesis that “mean difference between observed demands and estimated demands is

52

zero” is rejected. The null hypothesis couldn’t be rejected in the Rotterdam model and
AIDS model.

Table 4.2 Comparison of per capita pork consumption estimates (pounds) different
demand models (1970 to 1999)*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year Observed Rotterdam AIDS Log-linear
1971 60.2 61.2 61.0 54.1
1972 54.3 56.5 53.9 48.4
1973 48.5 47.4 49.3 48.6
1974 52.4 52.6 52.0 52.3
1975 42.7 44.1 42.8 47.0
1976 45.1 45.0 44.5 51.6
1977 46.7 46.4 47.3 52.3
1978 46.5 47.3 46.6 50.5
1979 53.2 51.7 53.5 52.5
1980 56.8 56.5 57.6 53.1
1981 54.2 53.9 54.1 49.4
1982 48.6 48.7 48.3 47.7
1983 51.3 50.4 51.2 51.7
1984 51.0 52.2 51.8 52.3
1985 51.5 51.2 51.3 50.6
1986 48.6 48.6 49.0 50.1
1987 48.8 47.3 48.7 49.6
1988 52.1 51.7 52.8 53.1
1989 51.5 52.3 52.3 51.6
1990 49.4 48.5 48.8 48.1
1991 49.9 49.2 49.7 50.1
1992 52.6 52.6 52.8 52.3
1993 52.0 52.3 52.2 - 50.7
1994 52.7 52.6 52.6 51.1
1995 52.2 52.1 52.5 50.8
1996 48.9 48.6 48.9 49.1
1997 48.5 47.6 48.6 50.1
1998 52.3 54.1 52.8 53.9
1999 53.7 53.9 53.4 51.1

 

 

 

 

 

 

*Observed and estimated per capita pork consumption.

The R-squared value is larger in the Rotterdam model compared to the AIDS and
the log-linear models. However, these R-squares can be misleading. Dependent

variables (left-hand side in the empirical equations) are different in the AIDS and the

53

Rotterdam models therefore the explanatory variables are not describing the same thing.
The log-linear model has a different set of dependent and explanatory variables. Based
on the forecasting errors and paired T-Test, both the Rottterdarn and the AIDS model are
superior to the log-linear model.

Now, we face the challenge of deciding which of these two models (Rottterdam
and AIDS models) to pick to estimate the pork demand. The AIDS model (equation 23)
estimates the change in consumption share of pork in a given year and we need to derive
the quantity of pork demand indirectly and the calculation is more complicated.
Estimation of pork demand by the Rotterdam model (equation 22) is more direct. The
predicted dependent variable, if divided by the average pork consumption share, results
the change in the log of pork consumption. Estimation of pork consumption and the
elasticity are more direct. Alston and Chalfant (1993) also concluded that the Rotterdam
model was superior to the AIDS model based on the specification tests. Because of
simplicity and the recommendation by Alston and Chalfant, the Rotterdam model was

chosen for fm'ther analysis.

4.6 Empirical estimation of pork demand

Systems of simultaneous equations consisting of pork, beef, chicken and fish
demands were used to estimate the per capita consumption of pork in the U.S. (Appendix
4.3). The three stage least-square procedure of econometric estimation was used to solve
the simultaneous equations. The parameter estimates for the pork demand equation are

listed in the following table (other meats were also included in the econometric model).

Coefficients, associated with beef price (y31), chicken price (1132), pork price (y33 ), fish

54

price (734), and income ([33) have expected signs.

Table 4.3 Parameter estimates for pork demand by Rotterdam model (1970 to 1999)

 

 

 

 

 

 

 

 

 

 

 

 

Parameter Estimates Error (Robust) P Value
F3 -0.0007 .0013 0.582

)3. 0.1731 0.0173 0.000**
732 0.0117 0.0077 0.131

)3, -0.1907 0.0195 0.000**
1’34 0.0059 0.0109 0.584

[33 0.4537 0.0514 0.000**
R2=0.94 Chi-square= 500 P=000 RMSE= 0.007

Note: Parameters associated with beef and chicken demands are given in Appendix 4.3.
Beef is a substitute of pork and the quantity of pork demanded goes up with

increased price of beef. Fish and chicken are also substitutes for pork and the regression

coefficients, y34 and y32 are not statistically significant at the five percent probability
level. The regression coefficient associated with pork price (y33) is negative and

statistically significant as expected. The coefficient (B3) related to the Divisia Volume

Index, which is a proxy for personal income is statistically significant in explaining the
per capita pork consumption. The regression procedure used to estimate the system of
equations is given in Appendix 4.3. A graphical representation of estimated and
observed per capita pork demand is given in Fig. 4.1 to compare the predictability of the

model.

55

 

Figure 4.1: Estimated and observed per capita pork consumption (l972-l999)*

 

 

 

 

 

o Rotterdam A Observed
61.2 r
42.7 4
l 1 f I
1971 1999
Year

 

*Estimated by Rotterdam model, forecasting error—=1 .75%

Estimated and observed per capita pork consumption, for 29 years were compared to

examine the strength of the Rotterdam model. Sixteen observed values were greater and

11 were smaller than the estimated values and two values were equal. The average

difference between these two series was only 1.69 pounds. The average estimated

demand was 50.92 pounds and the average of the observed demands was 50.90. On the

basis of T-Test and F-Test, we fail to reject the hypothesis (Ho) that the means and

variance are equal (Table 4.1). In other words, the hypothesis that “the mean differences

between estimated and observed values are equal to zero” was failed to reject. Therefore,

we may conclude that Rotterdam model as written in equation 22 is able to predict the per

capita pork demand.

56

 

Consumption figures listed above in Table 4.4 and Table 4.5 represent the national
average per capita consumption. We wanted to estimate per capita consumption for each
state in the U.S. Demographic composition of the population e. g. age, sex and ethnicity
also influence the consumption decisions. These variables were not included in the
system of equation above. The predicted pork consumptions were augmented to reflect
the difference in demographic characteristics by geographical regions. Differences in sex
and age of individuals on the consumption pattern can be perceived from Table 4.4
below, which was obtained from the Continuing Survey of Food Intakes by Individuals
by USDA (http://www.barc.usda.gov/bhnrc/foodsurvey/home.htm).

Table 4.4 Quantities of pork consumption by regions and selected age/sex groups"

 

 

 

 

 

 

 

 

 

 

Age group Pork Consumption (gram/day/head)
Northeast Midwest West South USA Average

5 and under 4 5 3 5 4.25

Males (20+) 15 19 12 13 14.75

Males (60+) . 14 22 16 14 16.5

Females (20+) 11 16 6 10 10.75
Females (60+) 10 13 8 10 10.25
Weighted Average 12.6 18.2 13.1 17.7 15.8
Population Share 19.6 23.5 22.0 34.9 100

Demand adj.factor 0.798 1.15 0.829 1.12 1.0

 

 

 

 

 

 

 

 

*Compiled from continuing survey of food intakes by individuals, USDA, 1994-96

57

Geographic regions with a higher percentage of children and females have a
tendency of lower per capita pork consumption. Females, who are sixty and older
consume a smaller amount of pork in comparison to their male counterparts. Similarly,
the regional consumption pattern also is interesting to note. The Midwest is highest in
average per capita pork consumption followed by the South. These factors are used to
adjust the regional estimates of per capita pork demand. Based on the relative differences
on pork consumption in 1994-96, adjustment factors are computed for each region.

Adjust. factor = reglonaIAveragemezghted)

 

nationalAverage(weighted)

Estimated per capita pork consumptions are then multiplied by the corresponding
adjustment factors. The adjustment factor of 0.798 (i.e. 12.6 regional average divided by
national average 15.8), for Northeast regions, for example, implies that other things
remaining constant, per capita pork consumption in the Northeast is 20.3 percent lower
than the national average. The adjusted total pork demands by different states are
presented in Appendix 4.4.

Table 4.5 Estimated regional pork demands 1997

 

 

 

 

 

 

 

 

 

 

 

 

Pork Demand (pounds)

Region Observed Estimated
Eastern Corn Belt 2,365,334,718 2,669,658,196
Western Corn Belt 378,547,354 323,063,273
South 4,956,71 1,107 5,448,498,609
Northeast 2,525,595,208 1,982,982,793
West 2,853,627,723 2,324,559,260
Total U.S.A. 12,987,504,940 12,748,762,131

 

Note: Aggregated from Appendix 4.4

58

The Bureau of Economic Analysis grouped the states into four geographic regions
namely Midwest, Northeast, South and West. The Midwest region is divided into the
Eastern Corn Belt and Western Corn Belt regions to make the groUping consistent with
following chapters of this dissertation. The estimated regional demand is listed in Table
4.5. The calculated demand is based on the adjustment factors (Table 4.4), population,
and estimated per capita pork consumption (Table 4.2). Aggregated demand of pork in
1997 is highest in the South followed by the Eastern Corn Belt region. Demand estimates
by states are listed in Appendix 4.4.

4.7 Pork export demand

According to the U.S. Foreign Agricultural Trade Database, annual increase in
pork production in the U.S. is approximately two percent. With increasing production,
total domestic consumption is also increasing at a slower pace. In contrary to domestic
demand, export demand for pork has been increasing rapidly and the U.S. is now a net
exporter of pork. The export market has become an increasingly important outlet for the
U.S. pork industry in recent years and its importance will further increase in the future.
According to the United States Meat Export Federation’s estimates, exports have added
about $6 /cwt to the price of that the American producers receivel 1. Table 4.6 shows the

annual export of pork from the U.S. to some of the countries or geographic regions.

 

l 1 www.usda.gov/oce/waob/outlook98/speeches/033/

59

Table 4.6 Pork export to selected countries from 1989 to 1997 (metric ton)

 

Annual pork exports (metric ton)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Country 1989 1990 1991 1992 1993 1994 1995 1996 1997
Australia 77 155 206 215 71 16 19 80 1,378
Canada 4,404 7,273 I8,1 13 9,023 1 1,008 16,321 17,528 29,677 41,804
China (Mainland) 177 206 517 120 30 49 196 741 2747
China (Taiwan) 283 85 125 57 129 162 4,935 9,824 2,397
13. Europe 655 4,235 1,224 3,067 864 1,064 1,959 1,088 961
Llapan 50,934 43,499 41,451 73,855 78,792 85,513 131,700 178,792 162,576
Latin America 30,943 22,427 36,484 46,342 35,448 58,392 31,264 29,909 39,342
1Mexico 23,363 14,604 28,442 €7,905 28,999 50,642 20,962 F2526 29,877
Netherlands 224 107 126 144 153 228 864 488 491
World 92.806 82,187 93,752 140,238 148,469 177,313 263,895 305,875 324,507

 

 

Source: Compiled from the U.S. Foreign Agricultural Trade Database.

Annual U.S. pork export has increased from 92,806 metric tons in 1989 to

324,507 metric tons in 1997, which is a 250 percent increase in eight years. Asia has

been the most important market for U.S. pork. In 1997, Japan imported 514,000 metric

tons of pork and 162,576 metric tons of the total import was from the U.S. The U.S. is an

important pork exporting country to Japan. The growth in U.S. pork export can be

visualized by the following figure:

60

 

Fig. 4.2 Annual U.S. pork export demand (1989-1997)

100.000

 

Year1$9 1933 1991 1992 1993 1994 1995 1% 1997
There is a huge potentiality for international market growth for pork. The

Chinese market can become an important outlet for U.S. pork. The current living
standard in China is almost equal to living standards in Taiwan 25 years ago. This fact
suggests that when economic development in China reaches the level of Taiwan, the
Chinese economy is projected to be greater than the combined economies of the U.S.,
Canada, the European Union, and Japan (Hayes and Clemens, 1997). It is projected that
China’s pork imports will grow to about nine million metric tons by the year 2007. Other
important U.S. pork importing countries/regions are Mexico, Canada, Australia,
European Union, and Latin America. The USDA projects that U.S. pork exports in the

year 2005 will be approximately double the current level of 324,507 metric tons.

61

4.8 Pork import

Although the U.S. became a major pork exporting country recently, it still imports
pork from different countries. According to the Foreign Agriculture Service, total pork
imports are listed in Table 4.7. Import figures are relatively smaller in comparison to the
exports.

Table 4.7 U.S. pork net exports, 1992 to 1997 (metric tons)

 

 

 

 

Year 1992 1993 1994 1995 1996 1997
Import 293 336 337 301 280 274
Export 140,238 148,469 177,313 263,895 305,875 324,507
Net Export 139,945 148,133 176,976 263,594 305,595 324,233

 

 

 

 

 

 

 

 

 

Source: FAS online (http://www.fas.usda.gov/dlp2/circular/1997/97-03/porkimpo.htm)
Net pork export can now be obtained from subtracting the annual import from the

total export. Net export quantity will be treated as a demand from a separate region in the

partial equilibrium model in Chapter Seven. A total net U.S. pork export in 1997 was

324,233 metric tons (713,312,600 pounds).

62

Chapter 5

V. Regional competitive position of pork industry

Expansion of an industry in different geographical areas arises because of cost
advantages associated with production and marketing. In the pork industry,
industrialization has contributed to productivity gains. Economic incentives, through
lower production costs exist in many areas for improving the efficiency of the hog
operations. The pork industry has additional economic benefits from further increases in
coordination between the production and packing stages. An assured large, stable flow of
uniform, high quality hogs to the packing plant can reduce pork production costs and
satisfy consumer demand for high quality pork products (Martinez, 1999).

Economies of scale obtained by technological innovations have further
contributed to the per-unit production cost reduction. The dramatic increase in hog
production in the Southeast is contributed by the increase in contracting in hog
production and the decline in tobacco industry. North Carolina farmers for example,
quickly accepted contracting in hog production because of the state’s familiarity with
production contracts in poultry. Contracting operations stabilized farm income in the face

of potential loss in tobacco revenue (Hurt, 1994).

5.1 Economics of production

Production costs include costs of cash expense items and costs related to capital
investments (fixed costs). Variable costs, such as feed, labor, veterinary and medicine,
fuel and the fixed costs such as farm overhead, taxes, insurance, and interest are

accounted as cash expenses. Capital replacement cost is the amount that is set aside each

63

year so that capital items can be replaced over time, in order to remain in business for the
long term. Non-cash expenses such as unpaid family labor and opportunity costs of land
are also accounted in production costs.

Total revenue from hog operations is calculated as the average price of a unit of
pork (e. g. hundredweight) times the number of units sold. It is assumed that the
individual hog firms are price takers under the perfectly competitive market. Under this
assumption, the total revenue curve will be an upward sloping and straight line. Both the
total cost function and total revenue function determine the profits from hog operations.
Where the difference between total revenue and the total costs is maximum, is the
optimum level of production. The production level where marginal cost is less than
marginal revenue (unit price), the firms are giving up the profits. Similarly, if the
marginal revenues are less than the marginal costs, firms are bearing unnecessary losses
(Cramer and Jensen, 1997). Profitability of hog production operations is, therefore,

affected by input costs, and the price of pork received.

64

Figure 5.1: Short-run equilibrium with three different firms

 

 

 

 

 

 

 

MC MC MC
P p P AC3
AC1
AC2
P" /
MR V
Q1 Q2 Q3
Firm 1 Firm 2 Fm“ 3

Figure 5.1 represents three hypothetical hog operations/firms. Combinations of
price (P) and output (Q) that lie above the average total cost curve (firm 2) represent
positive profits and the combinations that lie below represent negative profits (firm 3).
The first firm is operating in zero economic profit condition where marginal cost (MC) is
equal to the market price (P*) and average cost (AC). Firm 1 and firm 2 remain in the
market where as firm 3 will exit the market in the long run if it still remains unprofitable
at P*. However, in short run, the firm may be better off to remain in business if its
average variable cost is lower than the market price (marginal revenue). It can cover

some of its sunk/fixed costs by remaining in the production business.

5.2 Feed supplies and hog production

Historically, pork production and processing operations have been concentrated in
the Corn Belt states, an area with surplus feed. Corn farms with pigs have been profitable
relative to other types of farms (Hayenga et a1, 1998). In the Corn Belt states, pig

production has been a value-adding enterprise on available grain supplies and utilizing

65

available labor. Recently, growth in production has occurred in areas outside the Corn
Belt, especially in North Carolina, Kansas and Oklahoma (Hayenga et al., 1998). It is
interesting to investigate why this change occurred. The possible reasons behind the pork
production location shifi out of the Corn Belt to the corn deficit states may be the
following:

0 Bulk grain-purchasing capacity: Larger firms have higher grain purchasing
capacity and per unit grain transportation cost decreases substantially with
increased volume.

0 Technological changes in production system: Adoption of advanced production
and management technologies helps to improve efficiency in production. Larger
production units that can more easily adopt advanced technologies have higher
production efficiency than their smaller counterparts.

0 Environmental constraints: Lower costs of compliance in some locations relative
to other locations and fewer environmental restrictions improve profitability.

0 Mechanical advances: Presence of modern high-speed feed mills for example
lower feed processing costs. Newer and larger operations are more likely to
install modern mills, which are cost efficient. Instead of upgrading the old
facilities, it may be convenient to start with new sets of operations.

0 Climate and soils: Higher costs of construction and higher energy cost during the
winter season in the Midwest region are disadvantages relative to other states.
Lower cooling costs in the summer in the Midwest may partly offset the higher
winter costs. Similarly, high humidity and high rainfall make manure

management more complicated.

66

About 60 percent of the total variable cost of pork production is
appropriated to feed. Corn is the single most important input in pork rations. Soybean
meal is the second important feed component. Iowa, Minnesota, and South Dakota are
the states where the corn prices are lowest among the major pork producing states.
However, the lower feed cost doesn’t guarantee the profitability of the pork operations
since several other factors also contribute to the competitive advantage of one area over
the others as described earlier.

Average prices of corn grain and soybean meal in some of the selected pork
producing states are listed in Table 5.]. Prices are higher in corn deficit, new emerging
hog producing states (e. g. North Carolina, Oklahoma, and Utah) relative to the traditional
hog producing states (e.g. Iowa, Illinois, and Minnesota). Prices of feeder pigs and labor
cost are relatively higher in traditional areas as compared to emerging areas as shown in
Appendix 5.4. Higher feed costs in southern and western states are partially compensated
by lower prices of feeder pigs and lower cost of hired labor.

One can reduce the total cost either by paying a lower price of an input or using
less of it. Therefore, production areas with higher feed cost can still be competitive if
they can increase the efficiency of feed. Feed efficiency is measured in terms of pounds
of feed used for per pounds of gain in hog’s body weight. Similarly, production costs are
expected to rise with increased labor use. Labor efficiency, hour worked per
hundredweight gain for hogs is generally improved by capital-intensive production
technologies. Regional differences in pigs weaned per litter, litters per sow, and
operation size are also important in production efficiency. These elements reduce the cost

of feeder pig production.

67

Table 5.1 Average prices of inputs in different regions (1994-1998)

 

 

 

 

 

 

 

Mkt.hog Corn price Soybean meal Wage Feeder pigs
Regions $/cwt $/bushels price $/bushels rate $/hr $/cwt
E. Corn Belt 45.22 2.54 13.89 6.49 84.17
W. Corn Belt 44.90 2.45 13.89 6.45 88.02
South 43.27 2.79 16.43 5.85 73.25
Northeast 42.11 2.84 15.20 6.10 88.08
West 49.66 2.99 22.20 6.47 83.38

 

 

 

 

 

 

Source: Calculated from Appendix 5.4

Commodity prices listed in this table are calculated from the prices listed in
Appendix 5.4. Market hogs are most expensive on weight basis in the West followed by
the Corn Belt. The Corn Belt has access to cheaper corn and soybean meal, which are the
important inputs for raising hogs. Lower labor cost in pork production in the Southern
region is due to lower wage rates. In addition to the direct production costs, firms incur
regulatory costs, which is an important consideration in modern hog business. Different
aspects of environmental regulations and costs of compliance are discussed in detail in

Chapter Six.

5.3 Pig feeding operations budgets (grow to finish)
As discussed earlier, there are different kinds of operations in pig production.
Pork production systems are commonly divided into three stages. These stages are:
0 Breeding sows operations (Breeding)
0 Early-weaned pigs operations (Nursery) and

o Feeding-to-finish operations (Finishing)

68

 

All these three stages of production can be in a single site (different facilities) or
in different sites. The feeder-to-finish production system is the most important since it
incurs the major share of production costs and adds most of the gain. These operations
produce 200-265 pound market hogs. These types of operations are easier to compare for
their relative profitability in different locations. In general, feeder-to-finish operations
have smaller net return per hundredweight gain. These operators buy feeder pigs, which
results in higher operating costs. On the other hand, farrow-to-finish operations have
higher overhead costs because these kinds of operations involve all three stages of
production and require more buildings and equipment.

Cost of raising hogs varies by type of operation, size, and other location specific
factors. One production unit cannot represent all other operations in entire region. A
direct survey of production units could be very expensive and is beyond the scope of this
dissertation. This research mostly uses the secondary data from USDA databases, costs
and returns survey (FCRS), and various university sources. Some data are based on
expert opinion and some are derived based on existing information, and assumptions.
5.3.1 Assumptions made in enterprise budgeting

The source of revenues for feeding to finishing operations is from the sale of
market hogs. The weight of market hogs is assumed to be 250 pounds per pig. Not all the
feeder pigs started in feeding operations survive until the marketing stage. A four-percent
death loss (expert opinion) is used in adjusting operating costs and revenue. The average
market weight per pig is assumed to be constant throughout the regions. The differences

in revenue come from market prices in different regions. Price of market hogs doesn’t

69

vary within a region and size of operations since the producers are price takers12 . The

product sold and the inputs used are homogeneous.

5.4 Enterprise budgets: Feeder to finish operations

5.4.1 Formulation of pig diets

Composition of com-based feed as presented in Table 5.4 is based on nutrient and
energy requirements of hogs. For example, to constitute 2000 pounds of feed for
growing hogs, we need to mix 1631 pounds of com, 321 pounds of soybean meal and
minerals and vitamins. Rations are formulated to meet the nutritional requirements of
hogs. Instead of corn grain, some pork producers may use barley and sorghum as a
substitute as mentioned above. However, barley constitutes about two percent of total
feed grain and use of sorghum is also limited in the U.S. Therefore, corn is taken as a
standard feed grain in this study. Composite feed is fed according to the age of hogs until
they are marketed.

Hogs undergo several physiological changes between weaning and finishing
(market weight). Daily feed intake increases steadily during this period. Physiological
changes of pigs during the growth are important considerations for feeding requirements.
Feed costs are derived on the basis of diets and average prices of corn and soybean meal.
In order to achieve maximum feed efficiency, it is necessary to feed well-balanced diets.
Different groups of pigs need different compositions and amounts of diets designed for

specific purposes. Hog diets can be classified in the following four categories:

 

12 This assumption is for a simplification of the model. Size of operation may indeed impact price due to
quantity premiums.

70

Sow diets: Designed for bred gilts and sows using corn, barley, sorghum or wheat
as the primary energy source) and the amount may vary by age and body weights.
In general 4 to 5 lbs per day is recommended.

Boar diets: The composition is similar to that of sow diets and the common
feeding level is 5 1b to 6.5 lb per day. Younger boars require more feed than older
boars because of their faster growth.

Baby pig diets: Diets used for weaning pigs at the age of three weeks (45 pounds)
or less. Nutritional requirements change quickly in this stage. Diets are based on
age and body weights. Different kinds of antibiotics are also supplied in diets for
these young pigs.

Growing-finishing diets (45 to 250 pounds pigs): In this stage, diets play an
important role in the quality of meat and weight gain. Consumers demand for lean
meat has resulted in greater efforts by breeders and finishers to improve the
quality of meat. High lean gain pigs gain a minimum of 0.75 pound of lean pork
per day from approximately 45 to 240 lb of body weights. In order to obtain high

lean gain, specially formulated diets with higher amino acids levels should be fed.

71

Several biophysical factors affect nutrient requirement for pigs. Such factors
influence amount of feed and nutrient concentration”. Such factors are:
' Temperatures (weather)
I Genetic background and sex
I Health status of pigs
' Quality of feed (presence of toxin and molds, nutrient contents)
' Feed additives and growth promoters
Temperature and housing conditions play important roles in determining the
nutrient needs for pigs. Pigs housed in open areas are exposed to greater fluctuation of
temperatures than those housed in confinement facilities. Maintenance energy costs are
higher in uncontrolled housing environments. Pigs of different genotypes and sex have
different production efficiencies and thus the different nutrient requirements. Similarly,
health status, pig feed quality, and growth promoters’ influence feed efficiency. Higher
feed efficiency of feeding operations lowers the total feed requirement per pig.

Table 5.2 Growing-finishing: feed usage by pig growth rate

 

 

 

 

 

 

 

 

 

 

Group (body weight Average daily gain (lb/day) from 45 to 250 lb

. 1.6 1.8 2.0
in pounds) Lb of feed per pig
Grower 1 (45-80) 90 80 75
Grower 2 (80-130) 160 140 125
Finisher 1 (130-190) 205 180 165
Finisher 2 (190-250) 240 210 190
Total 695 610 555

 

 

 

 

 

Source: Swine nutrition guide Nebraska Cooperative Extension Service/USDA.

 

13 http://www.asci.ncsu.edu:80/Nutrition/NutritionGuide/introd~1/intro.htm

72

 

Table 5.2 presents the feed requirements during growing to finishing phase
depending on the pigs’ growth rate, as suggested by Nebraska Cooperative Extension
service/USDA. If average daily gain is 1.6 pounds, then the total feed requirements will
be 695 pounds per pig to reach the market weight of 250 pounds. Pigs need only 555
pounds of feed to reach the same weight if the daily average gain is two pounds but the
ration will be more costly. Producers switch diets according to estimated pig weight.
Monitoring growth helps ensure hogs are provided with the right diet to get optimum feed
efficiency.

Table 5.3 Suggested diets for finishing swine using corn as the major grain source

 

 

 

 

 

 

 

 

 

 

Ingredients Weaning to 140 lbs body wt. 140 to 250 lbs body wt.
Pounds/ton % Pounds/ton %

Corn yellow 1454 73 1631 82
Soybean meal 44 % 492 25 321 16
Calcium carbonate 15 0.75 16 0.80
Dicalcium phosphate 29 1.45 22 1.10
Salt 7 0.35 7 0.35
Trace mineral-vitamin mix 3 0.15 3 0.15
Totals 2000 100 2000 100

 

 

 

 

 

Compiled from Pork Industry Handbook, Michigan State University Extension, # E-1130

73

 

Yellow corn is the primary energy source for pig diets. Sorghum or barley can be used as
substitutes for corn to some extent depending on their relative prices and availability.
Appendix 5.2 shows the top-ten barley and sorghum along with corn producing states.
Barley producing states such as North Dakota, Montana, Idaho, and sorghum producing
states such as Kansas, Texas, and Nebraska can use barley or sorghum in pig diets to
some extent. However, barley and sorghum-based diets are not as efficient as com-based
diets because barley and sorghum contain less energy and high fiber as compared to corn.
Even though, these three grains are substitutes for each other, barley and sorghum are not
widely used in pig nutrition in the U.S. Therefore, it is assumed in this study that all the
diets are com based.

Feeder pigs

It is assumed that all the finishing operations buy feeder pigs. Costs involved prior to the
growing phase are not included in the budgets. These costs are factored into the price of
feeder pigs. Price of feeder pigs in different regions including a few major pork-
producing states are presented in Appendix 5.4. Cost of feeder pigs is the second most
important variable cost after feed costs.

Labor costs

Labor cost is another important consideration in the hog/pork business. Labor
availability and wage rates differ by geographical locations. Difference in hired labor
costs comes from the amount of labor employed by the feeding operations and average
annual hourly wages of field and livestock labor in different states. Average annual per
hour wage rates of field and livestock labor in major hog producing states are given in

Appendix 5.4. The proportion of hired labor and unpaid labor (family labor and

74

management) per hundred hogs are assumed to be different by the size of operations.
Small-sized operations rely more on family labor whereas large-sized operations employ
a higher proportion of hired labor in total number of labor hours. Fringe benefits
especially the health insurance to the employee in Eastern Corn Belt and Northeast
production regions are generally higher than the other production regions.

Overhead costs

Opportunity costs of unpaid labor, capital recovery of machinery and equipment,
opportunity cost of land, taxes and insurance, and general farm overhead come under
overhead costs. Differences in overhead costs are greatly influenced by the economic
opportunities of family labor, land values, government policies on income and property
taxes.

Utility costs

Climatic conditions in the production locations contribute in regional differences in cost
on facility construction and temperature control. Figure 5.2 illustrates the importance of
temperature control for proper grth of hogs. Different sizes pigs (ages) require
different air temperature ranges, for better performance. Smaller pigs up to 40 pounds
require higher temperatures than the larger pigs. Larger pigs have an optimum feed
efficiency when temperatures are between 50-70 degrees F (ASAE standards, 1997). The
optimum temperature zone has narrower range for younger pigs. Older pigs can resist a

wider range of temperatures.

75

In addition to temperature control, proper ventilation, relative humidity, and
sanitation are important considerations for efficiency in pork production. Figure 5.3
shows the requirement of ventilation in various outside temperatures to control heat and
humidity in the pork feeding facilities (adopted from Jones, 1996).

Costs for fuel and electricity, and buildings and equipment are related to
environmental control in pork feeding operations. However, the costs of heating and
insulation in colder locations mostly offset the cost of cooling and ventilation in warmer
locations (expert opinion). Regional differences in cost associated with the temperature,
humidity, and ventilation are indirectly captured by the utility costs that are listed in
enterprise budgets (Appendix 5.6A to 5.6C).

Figure 5.2 Approximate optimum temperature zones for pigs

90

Temp
\ (—-------- Feeding to finishing-------)
75 .1 \

Optimum temperature Zone for swine

 

60 "’

 

45 "

 

 

 

 

0 4 8 12 16 20 24 28
Age in Weeks

Source: ASAE Standards, 1997.

76

Figure 5.3 Ventilation curves for pig feeding operation.

 

 

 

 

 

 

 

 

HeatControl

A
E .
,2, Masture control
*E
.8
3
:3
8
r»

10 20 30 40 50 60 70 80

Outside tanperatu'es in F

 

 

 

 

 

 

Source: Cooperative Extension Service, Purdue University, 1996.
5.4.2 Enterprise budgets by regions and size of operations

Costs of raising hogs in different production regions were compiled. The budgets
are presented in 100 hog basis. This makes it easier to compare costs and revenues
across regions and size of operations. Three different scenarios by size of operations are
considered for cost comparison. The medium size of operations is considered as the base
scenario. An adjustment in variable costs and overhead costs are made to represent the
budgets for small and large-sized finishing operations in all regions and budgets are
modified to capture the economy of scale. The state level inventory data were obtained

from a USDA database.

77

Table 5.4 Hogs inventories by size of operations in selected states in 1997

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

State Small (<1000 Head) Medium (1000-4999) Large (>5000 Head)
% % %
AR 11 46 43
GA 23 33 44
IL 33 42 25
IN 31 42 27
IA 30.5 43.5 26
KS 25 23 52
KY 33 38 29
MI 27 43 30
MN 30 40 30
MO 22 24 54
NE 41 33 26
NC 2 26 72
OH 51 37 12
OK 6 12 82
PA 27 46 27
SD 40.5 25.5 34
W1 56 38 6
Other States 22 22 56
US 25 35 40

 

Source: USDA (http://www.nass.usda.gov:81/ipedbl).

The numbers in Table 5.4 show that most of the pork production operations in
the U.S. have more than 5,000 hogs and fall into the category of large. All the hog
operations are categorized as small, medium or large, based on the number of hogs in

inventory. It is interesting to note the regional differences in size of operations. Southern

states such as North Carolina, Arkansas, Georgia, and Oklahoma have a higher

percentage of hog inventories in larger operations. Midwestern states such as Iowa,
Indiana, Wisconsin, Nebraska, and Michigan have more hogs in small to medium sized
operations. Costs of raising hogs in these three categories are calculated separately by the
enterprise budgeting approach. Table 5.5a to 5.5e summarize the 1998 enterprise budgets

representing feeding operations in different regions and size of feeding operations. A

78

 

sample of detailed enterprise budgets by locations is given in Appendix 5.6.

Table 5.5a Feeder to finish system: cost and return per 100 hogs, E. Corn Belt*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Items Small Medium Large
Quantity fi Amt Quantity $ Amt Quantity $ Amt
Market hogs (cwt) 240 10,851 240 10,851 240 10,851
Corn (BU.) 938 2,252 885 2,252 885 2,252
Soybean meal (cwt) 134 1,856 126 1,751 126 1,751
Other feed cost 296 279 279
Feed cost 4,397 4,282 4,282
Hired labor (hr) 29 187 36 231 61 398
Unpaid labor (hr) 86 780 53 667 21 255
Total labor (hr) 1 15 967 89 898 82 653
Compliance cost** 31 81 105
Veterinary med. 106 78 57
Total variable cost (VC) 10,487 8,988 8108
Overhead cost (OC) 3,067 2,378 1,966
Total cost (TC) 13,505 11,366 10,074
Rev. less TC -2,653 -513 778
Rev. less VC 414 1,864 2,743

 

 

*Values may vary by each state in the region

** Costs listed in Table 6.6 as calculated by averaging the costs listed in Appendix 6.3.

79

 

Table 5.5b Feeder to finish system: cost and return per 100 hogs, W. Corn belt*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Items Small Medium Large
Quantity $ Amt Quantity $ Amt Quantity 8 Amt
arket hogs (cwt) 240 1 1,003 240 1 1,003 240 1 1,003
Corn (BU.) 938 2,194 885 2,194 885 2,194
Soybean meal (cwt) 134 1856 126 1,751 126 1,751
Other feed cost 296 279 279
Feed cost 4,376 4,224 4,224
Hired labor (hr) 21 137 28 181 50 323
Unpaid labor (hr) 64 715 42 527 17 208
Total labor (hr) 85 852 70 708 67 531
Compliance costM 31 81 105
Veterinary cost 133 98 71
Total variable cost (VC) 10,652 9,127 8,168
Overhead cost (OC) 3,101 2,108 1,790
Total cost (TC) 13,752 11,235 9,958
Rev. less TC -2,750 -232 1,095
Rev. less VC 351 1,876 2,835

 

 

*Values may vary by each state in the region

** Compliance costs listed in Table 6.6 as calculated by averaging the costs listed in

Appendix 6.3.

80

 

Table 5.5c Feeder to finish system: cost and return per 100 hogs, South*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Items Small lMedium Large

Quantity $ Amt Quantity $ Amt Quantity 5 Amt
lMarket hogs (cwt) 240 10,385 240 10,385 240 10,385
porn (BU.) 938 2,653 885 2,503 885 2,503
Soybean meal (cwt) 134 2,195 126 2,071 126 2,071
Other feed cost 295 279 279
Feed cost 5,144 4,852 4,852
Hired labor (hr) 19 1 1 1 26 155 46 267
Unpaid labor (hr) 57 468 40 326 15 126
Total labor (hr) 76 579 66 481 61 393
Compliance cost“ 31 119 108
Veterinary cost 115 85 62
Total variable cost (VC) 10,592 9,136 8,266
Overhead cost (OC) 2,288 1,586 1,384
Total cost (TC) 12,880 10,722 9,651
Rev. less TC -2,495 -337 734
Rev. less VC -207 1,248 2,118

 

 

* Values may vary by each state in the region

"Compliance costs listed in Table 6.6 as calculated by averaging the costs listed in

Appendix 6.3.

81

 

Table 5.5d Feeder to finish system: cost and return per 100 hogs, Northeast*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Items Small Medium Large
Quantity $ Amt puantity $ Amt Quantity $ Amt

|iMarket hogs (cwt) 240 10,040 240 10,040 240 10,040
Corn (BU.) 938 2,665 885 2,514 885 2,514
Soybean meal (cwt) 134 2,031 126 1,916 126 1916
Other feed costs 296 279 279
feed cost 4,992 4,709 4,709
Hired labor (hr) 34 207 46 283 78 478
Unpaid labor (hr) 102 1323 70 803 27 301
Total labor (hr) 136 1530 116 1086 105 779
Compliance cost" 39 195 1 13
Veterinary cost 80 59 43
Total variable cost 11,218 9,685 8,686
Overhead cost (OC) 3,288 2,992 2,992
Total cost (TC) 14,506 12,677 11,678
Rev. less TC -4,466 -2,637 -1,638
Rev.less OC -1,178 354 1,354

 

 

*Values may vary by each state in the region.

** Compliance costs listed in Table 6.6 as calculated by averaging the costs listed in

Appendix 6.3.

82

 

Table 5.5e Feeder to finish production system: cost and return per 100 hogs, west*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Items Small lMedium Large

Quantity $ Amt Quantity $ Amt Quantity $ Amt
lMarket hogs (cwt) 240 11,208 240 11,208 240 11,208
Corn (BU.) 938 2,807 885 2,648 885 2,648
Soybean meal (cwt) 134 2,831 126 2671 126 2,671
Other feed costs 296 279 279
Feed cost 5,934 5,598 5,598
Ilired labor (hr) 18 112 25 158 42 108
Unpaid labor (hr) 52 827 37 620 13 376
Total labor (hr) 70 939 62 741 55 484
Compliance cost“ 31 81 105
Veterinary med. 145 57 107
Total variable cost (V C) 12,509 10,784 9,802
Overhead cost (OC) 3,291 2,105 1,740
Total cost (TC) 15,801 12,889 11,542
Rev. less TC -4,592 -1,681 -333
Rev.less VC -1,301 741 1,406

 

*Values may vary by each state in the region.
** Compliance costs listed in Table 6.6 as calculated by averaging the costs listed in

Appendix 6.3.

83

 

Amount of feed is assumed to be the same across the regions. However, amount
varies among the sizes of feeding operations. Smaller operations (fewer than 1000 pigs)
are less efficient in feed than the medium (1000-4,999 pigs) and large (more than 5,000
pigs) operations. Overall, six percent more feed cost is considered in smaller operations.
Quantity and costs of corn and soybean meals are included in tables. Other feed costs
include the cost of minerals and vitamins that are mixed in the pig’s diets.

The quantity of hired labor hours varies by regions and size of operations. The
number and hours of labor employed are dependent on the type of technology used in
pork feeding operations, wage rates, and labor availability. Total labor hours consist of
hired labor and family labor. The labor costs and corresponding labor hours are based on
the USDA’s commodity costs and return survey, 1998” Dollar amounts on hired labor
were divided by average wage rate in the region to obtain labor hour per hog. Similarly,
opportunity costs of labor were used to calculate hours of family (unpaid) labor used in
the production process.

Hisham El-Osta (1996) estimated the average opportunity costs (Table 5.6) of
farm labor for different regions using weighted least squares regression. Although these
estimations are for the 1988 fiscal year, we may assume that these costs have increased or
decreased proportionately in 1998 and can be used as information to compare the relative

opportunity cost of labor in different regions.

 

l4 Producers were surveyed about production practices and costs in 1998. Hog costs and return accounts
were prepared using a guideline by the American Agricultural Economics Association (AAEA) task force
on cost and return estimation.

84

Table 5.6 Estimated opportunity cost of unpaid family labor

 

 

 

 

 

Region Opportunity Cost ($)
South 8.24
West 1 5.74
Northeast 1 1.53
Midwest 12.49

 

 

 

 

Source: USDA, Technical Bulletin Number 1848, pp.l9

Traditional (old) technology requires more labor as compared to modern automated
systems of feeding. Labor costs in larger and smaller sized operations are adjusted from
medium (base) sized operations. It is assumed that larger operations require 73 percent
of labor hours as compared to mid-sized operations. Similarly, smaller operations are less
efficient and require 36 percent more labor than the mid-sized operations. These
adjustments are based on a publication from the Purdue Cooperative Extension Service.

Table 5.7 Cost of production comparisons by pork production system ($/th)*

 

 

 

 

 

 

 

 

 

Costs 1200 sow 600 sow 300 sow
(Large size) (Mid size) (Small size)
Total Feed 18.56 (100) 18.56 (100) 19.80 (106.68)
Total Labor 2.06 (72.54) 2.84 (100) 3.86 (135.92)
Total Direct 22.07 (100) 22.07 (100) 23.37 (105.89)
Total 34.25 (95.88) 35.72 (100) 38.63 (108.15)

 

 

Source: Compiled from “Positioning Your Pork Operation for the 21St Century”
*Numbers in parentheses are relative costs in percentage by sizes

Cost structure in three different sizes of Operations is for the farrow-to-finish
operation systems. These relative costs are extrapolated to adjust the cost differential of
different sizes of feeder-to-finish production systems. The cost differential lies mainly in
feed costs due to differences in feed efficiencies, labor efficiencies, and in indirect costs

such as building and equipment. The cost differences are not due to the unit prices of

85

inputs but are due to the differences in their efficiencies. Overhead cost varies by
locations and size of operation. Six percent of additional overhead cost per pig is
assumed in smaller operations on the basis of Table 5.7.

Pork feeding operations of all sizes operate at a loss if we account all the cash
expenses and opportunity costs given the prices of all inputs and output. However,
producers get positive earnings if we consider only the variable costs. The Eastern Corn
Belt regions producers reap the highest operating profit ($1,861 per 100 hogs) followed
by the Western Corn Belt region and the West region ($1,661).

The results of production systems analyses as outlined above suggest that smaller
producers have limited ability to compete with larger producers on a cost of production
basis. The key to keeping hog business competitive is higher production efficiency.
Feed, labor, and building and equipment efficiencies are potential means of cutting
production costs. Smaller producers who do not attain strong efficiencies in production
are at a disadvantage relative to larger producers. Prices of inputs and output in one
location do not differ by size. All the firms are assumed as price takers and the individual

firm does not have market power to control the price of inputs and outputs.

5.5 Pork processing industry in regional competition

The pork processing industry is one of the determinants of the regional
competitiveness of the pork industry. Modern restructuring of pork processing facilities
has given the pork processing industry the ability to process large quantities of high-
quality pork products at competitive prices. The pork processing industry today is

characterized by a decreasing number of companies, the most profitable of which operate

86

P*

very large, relatively new, capital-intensive processing and packing facilities (Martinez,

1999). Packing costs decrease by size of the plants, but the procurement and

transportation costs rise. Improvement of vertical coordination offsets high procurement

costs (Cassell and West, 1967).

Figure 5.4: Old vs. modern processing plants

Price

Industry

Old plants

\

SAC

 

P** .

 

 

 
      

 

 

Qat: Q**

In Fig 5.3, old plants are operating in higher short run average costs (SAC)

 

D q

 

Modern Plants

SAC

 

 

(Cost structure of and modern pork processing plants)

whereas modern plants have lower average costs. Modern processing plants, due to

lower average costs, can remain competitive under the lower equilibrium market

(industry) price (P**). The lower average cost shifts out the industry supply curve,

forcing older plants out of business. Competitiveness of such facilities is critically

dependent on high volumes of raw product, because unit costs are driven lower as more

hogs are slaughtered (up to a certain range). In the current state-of-the-art packing

facilities, economies of size begin to be realized when four million hogs are processed

87

per year (ERS, 1996).

5.6 Locations of pork processing plants

The meat industry is one of the largest manufacturing industries in rural America.
Meat processing plants provide a substantial impact in rural economy. It is a source of
economic growth and many communities welcome meatpacking industries for their
impact on the local economy. On the flip side, meatpacking industries can pose
environmental threats and, hence, local, regional or state government limit their growth
by imposing various regulations. These two factors along with other many factors
contribute to shaping the industry structure. Pig slaughter and the pork processing
industry in the U.S. is becoming more concentrated and the number of plants is declining.
The number of pork processing firms reporting to the USDA in 1980 was 446 and this
number in 1995 declined to 209 (Hayenga, 1997). The few large pork-processing
companies are dominant in their market shares. Table 5.8 illustrates the recent market
share of five dominant companies in the pork processing sector.

Table 5.8 Plant capacities of the five largest slaughter firms in 1997

 

 

 

 

 

 

 

 

Rank Company Approx. Daily capacity Capacity share
(1,000 head) (%)
1 Smithfield 80.3 19
2 IBP 72.6 17
3 ConAgra 39.4 9
4 Cargill 37.8 9
5 Hormel 34.7 8
All other 160.6 38

 

 

 

 

 

Source: Hayenga et a1, 1998.

88

The largest five companies slaughtered 62 percent of total hogs in 1997. Smithfield and

IBP only captured 36 percent of the market share. Spatial distribution of pork processing

facilities is listed in the Table 5.9. All the hogs slaughtered by one company may not be

located in one geographic area. The table lists pork companies, their locations, and the

daily capacities in terms of number of hogs slaughtered. The average capacity of

processing plants by geographic regions is summarized in Table 5.10.

Table 5.9 Estimated daily slaughter capacities in different pork processing plants.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1998 1999 2000 (Head)
Company Plant State Capacity Capacity Cyacity Capacity Avetge
Smithfield Tar Heel NC 24,000 32,000 28,800 32,000 29,200
Smithfield VA 9,500 9,500 9,500 9,500 9,500
Cwaltney VA 8,800 8,800 8,800 8,800 8,800
Sioux Falls SD 15000 15000 15000 15000 15,000
Sioux CitL IA 15000 15000 15000 15000 15,000
IBP Waterloo IA 1 7,000 17,000 18,000 18,000 17.500
Logansport 1N 15,000 15,000 13.400 13,400 14,200
Storm Lake IA 13,400 13.400 13,400 13,400 13,400
Col.Junction IA 13,000 13,000 6,500 10,500 10,750
Madison NE 7,500 7,500 7,500 7,500 7,500
Pergl 1A 6700 6700 6700 6700 6,700
Swift Worthington MN 15.700 15,700 15,700 15,700 15,700
Marshalltown IA 15.700 15.700 15,700 15,700 15,700
Louisville KY 8,000 8,000 8,000 8,000 8,000
Excel Beardstown IL 16.000 1 6,000 16.000 16.000 16,000
Ottumwa IA 10,000 10.000 14500 14500 12,250
Marshall MO 1 1,800 1 1.800 8200 8200 10.000
Hormel Austin MN 16,000 16,000 16,000 16,000 16,000
Fremont NE 1 1,700 1 1.700 8500 8500 10.100
Rochelle IL 7.000 7,000 7100 7100 7,050
Farmland rete NE 8.300 8.300 8.300 8,300 8,300
Denison 1A 7,500 7.500 7,500 7.500 7,500
Monmouth IL 7.000 7.000 7,000 7,000 7,000
Dubuque IA 1 1.000 1 1.000 1 1.000 1 1,000 1 1,000
Seaboard Cuymon OK 8000 8000 15000 15000 1 1.500
Indiana Pack Delphi IN 13000 13000 1 1000 1 1000 12,000
Sara Lee West Point MS 6500 6500 6500 6500 6.500
Newbum TN 1500 800 2500 2500 1,825
Lundy's Clinton NC 8000 8000 8000 8000 8,000
Iowa Packing Des Moines IA 6000 6000 6000 6000 6,000
Chicago IL 1200 1200 2000 2000 1,600

 

89

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1998 1999 2000 (Head)
Company Plant State Capacity Capacity Capacity Capacity Average
Hartfield Hartfield PA 7000 7000 7000 7000 7.000
PremStd. lMilan MO 5000 5000 7000 7000 6,000
Clougherty emon CA 6000 6000 6000 6000 6,000
J .H.Routh Sandusky OH 3700 3700 3700 3700 3,700
Greenwood Greenwood SC 3000 3000 3000 3000 3,000
Sioux-Preme Sioux Center 1A 2650 2650 2650 2650 2,650
Johnsonville Watertown WI 1 800 1 800 l 800 1000 1,600
Mommence IL 500 1500 1,000
Pork packers Downs KS 1600 1600 1600 1600 1,600
Bob Evans Farms Bidwell OH
Xenia OH
Hillsdale MI
Galva IL 1500 1500 1500 1500 1,500
Yosemite Meat Modesto CA 1200 1200 1200 1200 1,200
Cloverdale Foods 1Minot ND 920 920 920 920 920
Leidy's Souderton PA 800 800 800 800 800
Owens Sausage Richardson TX 800 800 800 800 800
Odom's Little Rock AR 750 750 750 750 750
Abbeyland Foods Curtiss. W1 WI 700 700 700 700 700
Independent Meat Twin Falls ID 650 650 650 650 650
Brown packing Little Rock AR 600 600 600 600 600
Fineberg packing Memphis TN 500 500 500 500 500
lowell Packing Fitzgerald GA 350 350 350
asami Meat Co. Klamath Falls OR 300 300 300 300 300
Simeus Foods Forest City NC 300 300
arleton Packing Carleton OR 250 250 250 250 250
etzger Packing aducah KY 250 250 250 250 250
All companies Total 374,770 382,070 379,920 387,620 381,095

 

Source: Compiled from the National Pork Producer Council (N PPC).

90

 

Table 5.10 Regional distribution of pork processing capacity

 

 

 

 

 

 

Region Capacity (head/day) Capacity share

(percent)
Northeast 7,800 2.04
Eastern Corn Belt 83.850 24.57
Western Corn Belt 174,470 45.67
South 97,475 25.52
West 8,400 2.2

 

 

 

 

 

About 46 percent of the pork processing capacity lies in the Western Corn Belt
States (Iowa, Minnesota, Nebraska, South Dakota and North Dakota) only. Another 25
percent of hogs are processed in the Eastern Corn Belt and about 30 percent of hogs
processing capacity are out of the Corn Belt (South, Northeast and West). From the
above tables we may conclude that Smithfield and IBP are the most dominant companies
and the Corn Belt states are still the important states in pork production and processing.
The state of North Carolina (Southern production region) is also one of the dominant
players in the pork processing industry.
5.6.1 Pork processing cost

According to a survey of managers of the six largest firms and two firms with
new plants conducted by Hayenga in 1997, average estimates of fixed plant and
equipment costs were $6 per head for single-shift plants and $3 for double-shift plants.
Average variable costs were $22 and $20 per head for single-shift and double-shift plants
respectively. Labor cost is making up approximately 50 percent of total variable costs in
slaughter and processing. Therefore, total-processing costs in different locations are
greatly affected by wages paid to the slaughterers and butchers. Regional differences in

processing costs are calculated based on the wage rates of the workers employed in

91

animal slaughtering and processing facilities, and information obtained from the survey
by Hayenga (1997). The processing costs on a regional basis are given in Table 5.11 and
the pork processing costs by states are listed in Table 7.2 and Appendix 5.3.

Table 5.11 Regional pork processing costs, 1997

 

 

 

 

 

 

Region Processing cost Processing cost
$/Head $/cwt*
Northeast 25.88 10.49
Eastern Corn Belt 24.50 9.93
Western Corn Belt 25.50 9.83
South 25.26 10.34
West 26.50 10.74

 

 

 

 

 

*Compiled from ERS/USDA monthly hog slaughter data 1974 —l997.

In this chapter, we compiled regional differences in pork production and
processing costs. We gathered and discussed information that is relevant in pork
industry. Information we gathered was not complete and we made several assumptions in
our calculations. Production and processing costs and capacity constraints discussed

here in this chapter will be used in the transshipment model in Chapter Seven.

92

Chapter 6
VI. Environmental regulations and regional competition in the hog industry
The Census of Agriculture (1997) indicates that the number of hog operations in the U.S.
has decreased by half in the last 10 years, but the total inventory has remained roughly
unchanged as the remaining operations have become larger and smaller operations have
gone out of business. Due to the increased concentration of the industry, environmental
concerns related to hog production are rising in the U.S.

The hog industry in the U.S. faces the same regulatory pressure as other major
hog producing countries in Europe. However, unlike most of the European countries, the
U.S. hog industry enjoys the benefit of the availability of abundant land. Utilizing the
abundant land resource, hog producers in the U.S. can build newer and larger operations
that can better absorb manure and waste products. In this chapter, an environmental
stringency index is developed which can be useful in determining the relative regulatory

hardship among the various hog producing states.

6.] Hog production and manure management

Manure obtained from animal feeding operations is a good source of plant
nutrients. If used properly, manure can substitute for the commercial fertilizers that are
used for crop production. Nitrogen and phosphorus are primary plant nutrients that are
abundant in hog manure. Both these nutrients, however, can be harmful for water quality
if manure gets into the groundwater and/or surface water systems. Nitrogen is highly
soluble which can contaminate water through surface runoff, drainage and leaching

whereas phosphorus is less mobile and, only moderately soluble with water.

93

Integration of crop and livestock production is a crucial element of manure
management since the integrated enterprises use manure as a fertilizer in crop production.
Inability to utilize all the manure nutrients produced in the farm creates environmental
problems. It is not possible to incorporate all the manure generated from animal feeding
operations in limited croplands. Increased animal production even with proper waste
handling, raises environmental problems by increasing the size of potential waste storage
spills and raising the level of excess manure application (Innes, 2000). Once the
contamination has occurred, it is hard to remove the contaminants from the water.
Imposition of nutrient standards is costly and difficult to monitor, therefore manure
management is regulated through the required management practices and techniques in
the wastage collection, storage and field application (Metcalfe, 2000).

Because of ongoing structural changes (increased concentration) in animal
production, manure nutrient loading is on the rise (McBride, 1997). Increased concern
about the environmental effect of livestock waste is attributable to recent increases in the
concentration of livestock production (Pagano and Abdalla, 1995). According to ERS
(2000), in 1997, about 15 percent of very small farms and 72 percent of large operations
had inadequate capacity to utilize all the nitrogen produced from their operations and
these operations create greater risk of high nitrogen content in soil and ground water.
Almost all state governments impose restrictions on manure applications to some extent.
Nitrogen and phosphorus standards are the most common nutrient restrictions. According
to the Animal Confinement Policy National Task Force Survey (1998), the states of
Florida, Kansas, Michigan, Oklahoma, Pennsylvania, Texas, Vermont, Washington and

Wisconsin are concerned with phosphorus standards. Similarly, nitrogen standards are

94

imposed in Arkansas, Iowa, Illinois, Florida, Georgia, Kansas, Kentucky, North Carolina,
New Mexico, Missouri, Oklahoma, Pennsylvania, Rhode Island, Texas, Utah, Vermont,

and Wisconsin.

6.2 Regulations and hog industry relocation

The U.S. livestock industries face regulatory pressures from local, state and
federal govemment/agencies. Many U.S. states have their own set of regulations in
addition to federal regulations that shape the livestock industry. Environmental
regulations can vary among counties and even between townships within a state.
Compliance with environmental regulations may increase the cost of pork production and
hence decrease the net profits. It has been estimated that in the U.S. and the European
Union countries, the hog producers bear the extra burden of $0.40 to $3.20 per hog in
compliance costs, and that is up to eight percent of total hog production costs (Sullivan et
aL,2000)

Hog operations can reduce the total production costs by controlling compliance
costs. In order to achieve this goal, firms either need to change the existing production
practices to the practices that are environmentally friendly or move their operations to the
geographic locations that are less stringent and more friendly (locations where the
environmental regulations are less severe or are more likely met or locations where
compliance is easy to meet because of climate). There is a general belief that strict
environmental regulations drive industries out of some states into others. Some people
argue that to attract waste generating firms, some states adjust their regulation downward

to the mandated lower level, the federal regulation. Studies have shown that

95

environmental regulations are relatively unimportant compared to the other factors in a
firm’s location decision (Metcalfe, 2001). Traditional factors such as the level of
manufacttuing activities and energy costs are more important than the environmental
regulations on the location decision. However, environmental policy variables have larger
effects on location decisions than wages or taxes (Stafford, 2000). Unlike the
manufacturing sector, this may not be the case for the livestock industries and the

environmental factors may influence the industry locations.

6.3 Confined animal feeding operations and state regulations

Different local and state policies and other relevant laws and ordinances for
animal confinement operations influence the location of animal production. Hurt and
Zering (1993) reported that the regulatory factor was one of the important factors that
could explain the growth in swine industry in North Carolina. Favorable regulatory
factors allowed expansion of the hog industry in North Carolina earlier, but this is not the
case now. Environmental restrictions are getting tougher due to the excessive growth of
the hog industry in the state of North Carolina, which can be applicable to other states
also. There are too many regulations and ordinances, and there is a lack of systematic
analysis to reach definite conclusions. Furthermore, these regulations are not static and
are subjected to modification or removal over time. The listing of such regulations
together with their relative roles in animal feeding operations and deriving a stringency

index would be an important task to fill this gap in the literature.

96

Listings of the various regulations imposed by federal, state and local

governments for different states are presented in Table 6.2. A short description of the

regulations is given in Table 6.1. The particular legislations either not imposed (score 0)

in the state or imposed in the state (score 1) or extensively imposed (score 2) are

categorized. Some regulations, such as requirement mediation outside the court and

zoning exemptions are helpful to animal feeding operations. Such regulations are

assigned negative scores toward the calculation of a stringency index. The stringency

index derived here is based on the database published by “Animal Confinement Policy

National Task Force”. The national Center for Agricultural Law Research and

Information and the Task Force are still working on this database for verification.

Table 6.1 Descriptions of federal and state stringency

 

 

 

 

 

 

 

 

floodplain restrictions, soil borings, and

compaction.

 

Stringency Description Code

CAFO controversial Confined animal feeding operations are 1=yes, 0=no
usually controversial.

Corporation Corporations are prohibited in owning 1=yes, 0=no
farmlands or engaging in confined livestock
operations in the state.

Supply restriction Restrictions on packers owning or contracting 1=yes, 0=no
livestock supplies.

Waste and manure Require appropriate design and construction 2=yes, 0=no

management of the waste-collection and storage systems.
Require management plan for approval. Field
application plan of manure nutrients.

Geological testing Physiological or geological tests required e. g. 1=yes, 0=no

 

97

 

 

 

 

 

 

 

 

 

 

 

 

Stringency Description Code
Moratoria Restrictions on size of operations and 1=yes, 0=no
production in local area or the state.

Setbacks Minimum distance requirements for feeding 1=yes, 0=no
operations and manure/waste storage from
property lines and water sources.

Public hearing Needs public hearing in CAFO establishment 2=yes, 0=no
before the state approval.

Nutrient standards Restrictions on amounts of manure 2=yes, 0=no
applications, timing of land application, set
backs for application, and irrigation.

Odor standards Restrictions on number of objectionable days 2=yes, 0=no
per year.

Flies and insect Requires controlling flies and other insects 2=yes, 0=no
related to CAFO.

Mediation State require/provide mediation or arbitration -1=yes, 0=no
other than court system.

Exemption from State government exempt confined livestock -1=yes, 0=no

Zoning operation and manure application from zoning
authority.

Fees State government during approval process 1=yes, 0=no
assesses fees.

Training requirement State government imposes education or 1=yes, 0=no

training requirements for manure

management.

 

 

Population density

 

States with relatively higher population
densities are potentially more stringent to hog

industry (1-4 index)

 

4=very dense

1=not dense

 

98

 

Approval requirements for facility and waste management system plans are common in
most of the states. Animal feeding operations are required to prepare nutrient
management plans and they are required to comply with the standards based on the
nitrogen content of manure that is applied to the soil. Some states, e. g. Michigan and
Kansas impose the phosphorus nutrient standard. The phosphorus standard can be more
stringent because the application of manure requires more land since plants need less

phosphorus than nitrogen.

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103

Methods and timings of field applications are also important aspects of nutrient
management. Application of manure when the soil is saturated with water or the ground
is frozen can be detrimental to surface water and groundwater. The rate of manure
application in soil depends on several factors such as the soil absorption capacity and
requirements of plant nutrients especially nitrogen, phosphorus and potash. Rain and
melting snow can cause organic nitrogen to wash into streams if manure has been applied
to unprotected cropland. Excess phosphorus, attached to soil particles, can be carried into
streams by soil erosion. It has been recommended that liquid manure should not be
spread within 30 feet and solid manure within 15 feet of a watercourse. Population
density can be the important factor for environmental stringency. It is likely that highly
populated states are more concerned about the hog industry growth and put more
stringent regulation in future.

Tabulation of regulatory information in the above table allows for a state-by-state
comparison of the relative stringency level and ranks the states according to the
stringency index. Numbers assigned to regulations imposed are summed to create the
over all stringency indexes for the states. After constructing the index, states are grouped
into four groups to identify states of high stringency to low stringency and to assign
estimated environmental compliance costs as suggested by the U.S. Environmental
Protection Agency (U.S. EPA). Fig. 6.1 and Table 6.3 illustrate the environmental

stringency indices in the U.S.

104

Table 6.3 Environmental stringency grouping

 

 

 

Stringency States
Highly Restrictive Illinois, Kentucky, Maryland, Nebraska, North Carolina, Ohio,
(Index >15)
Oklahoma, South Carolina, and South Dakota.
Restrictive Arkansas, California, Connecticut, Florida, Indiana, Iowa, Kansas

(Index 11-15)

Minnesota, Missouri, Mississippi, Montana, North Dakota, Oregon,
Pennsylvania, Rhode Island, Tennessee, Texas, Utah, Virginia,

Vermont, Wisconsin, and Wyoming.

 

Moderately
Restrictive
(Index 6-10)

Arizona, Colorado, Idaho, Maine, Michigan, Nevada, New York,

New Mexico, and Washington

 

 

Little or

Nonrestrictive
(Index <6)

 

Alabama and New Jersey

 

This stringency classification is derived from the index developed in Table 6.2.

The index is very subjective, but it expresses the opinion of several experts. The

classification of states in different stringency groups is, therefore, not perfect. The states

of North Dakota and Nebraska, for instance, are classified as highly restrictive for

confined animal feeding operations. But it is less likely that these states will not expand

pork production. Similarly, the states of New York and New Jersey fall under a. less

restrictive category and the pork production in these states is less likely to expand.

Assigning the compliance cost to individual states based on the stringency index derived

in Table 6.2 is tedious, if not impossible.

105

 

The U.S. EPA has proposed regulations to reduce the water pollution from large
livestock operations. It is expected that the revision on the existing Clean Water Act will
reduce water pollution from agriculture, one of the leading sources of pollution. Under
the Clean Water Act, Concentrated Animal Feeding Operations (CAFOS) are considered
as point sources of pollution. In response to concerns about the contamination of rivers,
lakes, streams and other water sources from manure and other animal wastes, the U.S.
EPA and the USDA developed the Unified National Strategy for animal feeding
operations as part of the Clean Water Action Plan. Under this plan various alternative
options have been proposed to control point source pollution from CAFOS.

Figure 6.1: Environmental stringency grouping

   

US State 5

- Non-restrict iv e

M odera‘t e1 3; restrictiu e
Re stri cti we

[E] H ghly rest ['1 ct iue

The state of West Virginia, New Hampshire and Louisiana are not indexed for the

stringency due to the unavailability of data during this research. Dark colored states in

Fig. 6.1. (AL, and NJ) are little or non-restrictive. Similarly, the states of Arizona,

106

Colorado, Idaho, Maine, Michigan, Nevada, New York, New Mexico and Washington
are moderately restrictive production regions. The stringency grouping based on the
index developed in Table 6.2 is subjective. However, this classification gives some idea
on how friendly various states are to the hog producers. It is not possible to calculate the
dollar amount as a compliance cost by states from this index. It is possible that less
restrictive production areas may be more costly than more restrictive areas in waste
management and compliance because of differences in topography, soil and climatic

factors.

6.4 Description of technology options for manure management

In order to estimate the costs associated with environmental regulations, it is
important to analyze the available technology options for waste management. The
United States Environmental Protection Agency has suggested seven different technology
options depending on the vegetation, topography, soil types, hydrology, climatic
conditions and concentration of confined animal feeding operations.

Option 1: Nitrogen-based manure application: Nutrient management planning, land
application limited to nitrogen based agronomic application, lagoon depth markers,
periodic inspections, mortality handling, record keeping, soil sampling once every three
years, lOO-foot setback from surface water.

Option 2: Same as Option 1, but restricts the rate of manure application to the
phosphorus based rate. CAFOS that require phosphorus based land application incur
additional costs because they need to apply additional commercial fertilizer to fulfill

nitrogen requirements for crops and more land is required to spread the manure.

107

Option 3: Option 2 plus groundwater requirements. If beneath the production area, there
is a direct hydrologic connection to surface water, then that will require groundwater
monitoring and controls (e.g. installing monitoring wells, ground water sampling twice a
year, installing impermeable pads in manure storage areas).

Option 4: Option 3 plus requirements of sampling of surface waters adjacent to
production area and the area to which manure is applied.

Option 5: Option 2 plus zero discharge requirements from the production area that
doesn’t allow for an overflow

Option 6: Option 2 plus large operations require installing anaerobic digestion and gas
combustion to treat the manure.

Option 7: Option 2 plus prohibition of manure application to frozen, snow-covered or
saturated ground.

The U.S. EPA cost methodology report for swine and poultry sectors has
estimated the costs of installation, operation, and maintenance of several techniques and
practices that are required for regulatory options. The USDA’s representative farm
approach has been adopted to estimate regulatory compliance cost. The U.S. EPA has
estimated the costs to address those who need to implement an operation, technique, or
practice in order to meet proposed requirements. Frequency factors, based on regulatory
requirements, geographic location, type and size of operations, and status of the industry
are calculated. For example, the surface water monitoring frequency factor of 27.9 for
large operations in the Midwest indicates that 27.9 % of operations already have installed
surface water monitoring systems or they are required to install in the near future and

72.1% of operations do not need to invest into the system. The U.S. EPA has considered

108

all these facts to calculate regulatory costs for the swine industry. However, the details of
the methodology of calculation is undisclosed. The frequency factors that were
considered in compliance cost calculation by the U.S. EPA are given in Table 6.4.

Table 6.4 Management techniques required by swine operations by region's'

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

[Manggement technique F rgieng factors” (%)
IMW/mediumMW/LarngA/Medium A/Largq
Groundwater well installation 72.54 72.54 76.09 76.09
Surface water monitoring 4.60 27.90 5.70 17.90
Soil augur 0.00 94.00 0.00 94.00
anure sampler 0.00 71.90 0.00 71.90
anure spreader scale calibration 0.00 71.90 0.00 71 .90
Initial nutrient management plan 10.70 46.90 24.90 69.40
Recurring nutrient management plan 10.70 46.90 24.90 69.40
Soil testing 90.00 94.00 90.00 94.00
Groundwater links to surface water 1.10 23.10 7.00 12.30
Testing manure 2.10 38.30 6.10 29.90
Recordkeeping 71.00 98.90 93.10 99.90
Calibration of manure spreader 0.00 99.00 0.00 99.00
Groundwater maintenance monitoring 72.54 72.54 76.09 76.09
lMortality composting 72.54 72.54 76.09 76.09
Lagoon liners maintenance 72.54 72.54 76.09 76.09
Lagoon depth marker 0.00 99.00 0.00 99.00
Storm water diversion 72.54 72.54 76.09 76.09
Stream buffer maintenance 0.00 99.00 0.00 99.00
Visual inspection 0.00 25.00 0.00 25.00
Feeding strategies 14.9 67.70 17.80 72.70
Solid liquid separator 7.70 0.00 2.30 1.50

 

Source: Compiled from 2001-U.S.EPA data.

 

15. The U.S. EPA has classified geographic regions as: Mid-Atlantic (MA) region: MD,
ME, NC, NH, NJ, NY, PA, RI, TN, VT, WV, VA, CT, DE, KY, MA. Midwest (MW)
region: IA, IL, IN, KS, MI, MN, MO, ND, NE, OH, SD, WI. Central region: MT, WY,
ID, CO, UT, NV, AZ, NM, TX, OK. Pacific region: CA, WA, OR, AK, HI. Southern

region: AL, AR, FL, GA, LA, MS, SC
16 Frequency factors: Percentage of industry that already implements particular

operations, techniques, or practices required by the proposed environmental compliance

rules.

109

 

The U.S. EPA has estimated environmental compliance costs for 514 medium and
large feeder-to-finish operations representing 33,390 feeder-to finish operations in the
U.S. Table 6.5 summarizes the costs by technology Options and size of operation. The
data and method used to derive this table is discussed in Appendix 6.1. Different
technology options require different facility structures and equipment and hence incur
various levels of compliance costs. Costs can differ even under the same options due to
the variation in location, topography, soil type, land availability, and size of operation.

Table 6.5 Technology options for CAFOS and compliance costs*

 

 

 

 

 

 

 

 

 

 

 

 

Option Compliance costs ($/ hog)
Mid-Atlantic Midwest

Medium Large Medium Large
Option 1 0.47 0.31 0.16 0.46
Option 2 1.45 0.37 0.42 0.16
Option 3 2.42 1.36 0.77 1.14
Option 4 2.44 1.25 0.85 1.46
Option5 1.90 1.08 1.16 1.16
Option6 - 2.12 - 1.17
Option 7 1.59 0.74 0.64 0.52
Average 1.95 1.13 0.81 1.05

 

 

 

 

 

*Compiled from 2001-U.S.EPA data with additional assumptions

Compliance costs for medium-sized operations in Option 6 is not applicable
because this option is designed only for large operations that require installation of
anaerobic digestion and gas combustion systems. Option 2 is more stringent than Option
1 and option 3 is more stringent than the Option 2. In most of the cases, compliance costs
are consistent with the technology options or the level of stringency. However, this

condition may not hold in all cases. Option 2 in the Midwest region has smaller

110

 

compliance costs than Option 1, although Option 2 has additional restrictions over Option
1. It should be kept in mind the fact that hog operations required to comply with Option

1 and Option 2 are not necessarily required to be in the similar situations (e.g. soil type,
topography, land availability). Similar structures within a few miles distance may have
different costs due to the geological factors.

The U.S. EPA data analyzed above does not include an estimation of compliance
costs for small-sized hog operations. The U.S. EPA assumes smaller units do not need to
invest significant amounts in manure management to comply with environmental
regulations. However, this dissertation research assumes that the small hog operation is
also required to invest in manure management. Since the environmental impacts of small
hog operations are comparatively small, let us assume that a small operation’s
compliance cost is equivalent to the average costs in Option 1 in Table 6.5. Furthermore,
cost calculations are done only for the Mid-Atlantic (MA) and the Midwest (MW)
regions. According to the U.S. EPA cost structure in the South, Pacific and Central
regions would be equivalent to the costs in the Midwest region. With these assumptions,

we can assign environmental compliance costs to all states and regions.

lll

Table 6.6 Compliance costs by production region"

 

Compliance cost $/hog

 

 

 

 

 

 

 

 

 

 

Region Small Medium Large
Northeast 0.39 1.95 1.13
E. Corn belt 0.31 0.81 1.05
W. Corn belt 0.31 0.81 1.05
South 0.34 1.19 1.08
West 0.31 0.81 1.05

 

* Cost by states are listed in Appendix 6.3

The regional compliance costs in Table 6.6 were calculated by averaging the costs by

states as listed in Appendix 6.3. and these costs are linked to the feeder—to-finish

production system enterprise budgets in Chapter Five, Table 5.5 and Appendix 5.6.

112

 

Chapter 7

VII. Optimization of production and processing of pork

Many of the components described in the previous chapters are combined to minimize
the total cost of production, processing and distribution of pork in the U.S. to meet
demand. An interregional mathematical programming model is constructed. The costs
of production including the compliance costs of pigs are determined from enterprise
budgets developed in Chapter Five of this dissertation. Slaughtering and packing costs
are also compiled in Chapter Five. Transportation costs between the production regions
and the consumption regions are calculated based on the travel distance and information
obtained from trucking companies. The regression model in Chapter Four has estimated
consumption demand. Regional consumption estimates are obtained by multiplying
estimated per capita pork consumption and the population in the region. Export demand
is determined exogenously and the data were obtained from the USDA. Export demand
is treated as a separate consumption region in the mathematical model.

The processing capacity in each region is the sum of the existing capacities of
pork processing plants. The maximum quantity of pork a region could produce is
calculated on the basis of existing production. Some states and regions have the potential
for increasing their pork production level. However, government regulations (high
compliance cost or moratoria) will not allow a region to increase its pork production
beyond a certain limit.

Analysis of interregional competition in pork production is developed on the
principle of comparative advantage that deals with only one commodity, unlike the

regional comparative advantage that deals with several commodities (Mighell and Black,

113

1951). Interregional competition analysis determines the competitive position of various

regions that produce the same commodity.

7.1 Mathematical programming: economic environment

The comparative advantage can arise from various factors. The lower cost of
feeding hogs in each region is due to the availability of lower costs of feed, higher feed
efficiency, economy of scale, lower environmental compliance costs, and several other
factors favorable for pork production in one region over another region. Similarly, lower
processing costs and/or higher consumption demands can be advantageous to some
regions over other regions.

Takayama and Judge (1971) used interregional linear activity analysis, a
production and allocation model to address the regional competitive advantages. The
transshipment linear programming method used in this study is based on the model used
by Takayarna and Judge. The mathematical model, which minimizes the total costs of
producing, slaughtering, packing and transporting pork, has the following characteristics:
There are ‘n’ regions of production, processing and consumption. Hogs are primary
(intermediate) products and pork is a final product. Each region has a unit production cost
for raising hogs and these costs are known. The primary product passes through a
processing plant (slaughtered and packed) to convert to a final product (pork). The rate
which hogs are transformed to pork cuts is known and fixed for all regions. Each region
has a unit processing cost for processing pigs into pork and these processing costs are

known. A non-negative, known quantity of pork is demanded in each region.

114

Hogs and pork are mobile commodities whereas production facilities and
processing plants are immobile. Processing costs are in constant proportion for all output
levels and these costs may vary from one region to another. Distance separates all the
possible pairs of production, processing and consumption regions. The shipment costs
per unit of pigs and pork from each region are known. The supply of the final
commodity (pork) is equal to or greater than the total demand. All the pigs and pork are
homogeneous products and therefore, pork processors and consumers are indifferent to
the source of their supplies. Market prices of all the inputs and outputs are fixed in time

‘t’.

7.2 Mathematical model

In order to specify the transshipment model in mathematical form, the following
notations are used,

i,j are regions and i=1,2,3,4, ...... ,n; j=1,2,3,4 ...... ,n

F i = cost of feeding hogs (including environmental cost) in region i ($/cwt)

Bii = cost of transporting slaughter hogs from region i to j

S = cost of slaughtering/processing pigs in region i

Cii = cost of transporting processed pork from region i to j

Pi = number of finished pigs fed in production region i

Qij = number of pigs transported from production region i to processing region j
Xij = amount of pork transported from processing region i to market j

D, = consumption demand of pork in market i

115

Given the setting described above, the multi-regional allocation model now can be

written in mathematical form as,

Minimize
n n I! I? n n
ZFiPI+ZZ Berrj+ZISiXi+Zl ZCU’XU (7.1)
1= 1: j= I: I: j=
Subject to

R - ZIQ.) 3 O (7.2)
Q.- + 2 Q.) S R (7.3)
i=1

X, + 2 X0. 2 D, (7.4)

i=1

Pi’QHXi’XijZO (7.5)

Where,

Equation 7.1 is the objective function that we are minimizing.

Equation 7.2 indicates the maximum number of pigs a region can market (in the base
model, number of pigs marketed in 1997 are assumed to be the upper limit of the capacity
and we permit changing this limit in the scenario analyses).

Equation 7.3 is the number of finished pigs region i ships to itself and ships to other
regions is less than or equal to the number of pigs produced in that region.

Equation 7.4 denotes consumption demand for pork in region i is less than or equal to the

pork produced in region i plus the in shipments of pork from region j.

116

Equation 7.5 implies no negative production, shipment and consumption.
Assumptions:

Optimization: The objective function is minimized.

Homogeneity: All slaughter hogs and all the packed pork are homogeneous.
Proportionality: Unit costs of inputs are constant and do not depend upon volume.
Determinism: All the coefficients in objective functions are known constants.
Additivity: No interaction effects between activities.

Continuity: Activities and resources can also be in fractions.

F initeness: There is a finite number of activities and constraints.

The assumption of additivity and proportionality together define linearity in the activities.
The linearity property of production function leads to constant returns to scale.

The mathematical model described in equation 7.1 to 7.5, now can be solved to
find the optimal solution by Lagrangean method”. The Kuhn-Tucker conditions must
hold for the optimum solution. The conditions state that in order to obtain efficient
activities, regional market prices must be such that:

o Profits are zero on all production, processing and marketing activities

0 Market prices of live hogs and pork are positive only if regional availability is
equal to zero (If a region is producing more than the actual demand then the price
of the surplus is equal to zero and it has no economic value).

0 Rents on pork processing plants are positive only if the capacities in each case are

fully utilized.

 

17 For a detailed problem specification, necessary and sufficient conditions for optimality, see Chapter 1-6
in Partial and Temporal Price and Allocation Models by Takayama and Judge, 1971.

117

0 If there is a flow of a product (live hogs or pork) from region i to region j, then the
' difference in market price of these products in these regions is equal to the unit

transportation cost.

7.3 Transshipment model set up

7.3.1 Production regions

Hog feeding operations are distributed in all states in the U.S., although such
operations are highly concentrated in a few states as described in Chapter Three of this
dissertation. Most of the U.S. states in this analysis are considered as separate production
regions except where a few smaller states are combined and considered to be one
production region. Production sites where the most hogs are concentrated in each state
are the points of origin from where hogs are transported to the slaughter/processing
plants. Hereafter, if a production region is named with the state name it refers to the
“supply center” as indicated in Table 7.1.

Although a production region is competitive in terms of production costs, it
cannot grow its production infinitely beyond the carrying capacity of its natural
resources. Based on personal interviews with industry experts”, in the states of North
Carolina, South Carolina, Virginia, South Dakota, Nebraska, Missouri, and Delaware this
is “very unlikely” from the current level. Michigan and Colorado fall under the category
of “not likely to expand pork production”. The New England States (Maine, Vermont,
New Hampshire, Massachusetts, Rhode Island, Connecticut, New York, and New Jersey)
have lower potentialities to grow due to higher population densities. Growth in pork

production is more likely to occur in the remainder of the states. The number of hogs

118

marketed in 1997 by production regions and the possibility of expansion of production
are listed in Table 7.1. The number of hogs marketed can be misleading because hogs are
sometimes sold more than once. According to the industry experts, average number of
hogs slaughtered is 90 percent of the number of hogs marketed. There are some instances
when hogs are sold twice. According to the pork industry experts, approximately 10
percent hogs are sold twice. In order to avoid the double counting, the number of hogs
slaughtered is calculated as the 90 percent of the number hogs marketed. Therefore, the
production capacity of a region is assumed to be the number of hogs slaughtered.
Production regions are categorized from one through four on the basis of
expansion potential (1=almost impossible to expand, 2=not likely to expand, 3: less
likely to expand and 4=likely to expand). According to the industry experts, the states of
Missouri, North Carolina and South Carolina fall under category ‘one’ since the
expansion of the hog industry is very difficult in these states. Scarcities of land for
manure application, moratorium from federal and state governments, and already
concentrated hog businesses are some of the factors that limit the expansion. Table 7.1

shows the number of hogs sold and the number hogs actually slaughtered.

 

18. Dr. Laura M. Cheney, Department of Agricultural Economics, Michigan State University, personal
interview, August 2001.

119

Table 7.1 Production regions and number of ho s marketed in 1997

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Hogs Hogs Growth Production Supply
State Marketed Slaughtered Potential Concentration Center
AL 378,545 340.6905 4 Eastern Valley Jackson
AZ 394,924 3 55,4316 4 North Navajo
AR 1,126,268 101,364] 4 South West De Queen
CA 364,129 327,716.] 4 South Central Bakersfield
CO 1,492,986 1 34,3 687 2 Morgan organ
L 1 14,986 103,487.4 4 Central Gainesville
GA 1,100,078 990,070.2 4 South Central Albany
ID 75,778 68,2002 4 North West Lewiston
IL 8,028,400 7,225,560 4 North West Henry
IN 6,670,396 6,003,356 4 Central Anderson
IA 23,475,424 21,127,882 4 Central Des Moines
[KS 3,269,308 2,942,377 4 South West Stevens
lKY 1,135,250 1,021,725 4 idwest Davies
LA 64,030 57,627 4 entral Alexandria
, DE, NJ 204,545 184,090.5 4 astem Baltimore
[MI 1,732,164 1,558,948 2 South West Kalamazoo
[MN 8,990,979 8,091,881 4 South Central lMartin
[Ms 456,040 410,436 4 Central Columbia
[Mo 6,365,955 5,729,360 1 North Central Charlton
IMT 263,909 237,518.] 4 North Central Sweet Grass
NE 6,245,220 5,620,698 3 North East Columbus
NV 19,889 1 7,900.1 4 Western Sparks
NM 9,875 8,887.5 4 Central Albuquerque
NY 131,275 1 18,1475 3 West Genesee
NC 16,373,417 14,736,075 1 South Coastal Bladen
ND 325,051 292,545.9 4 South East Ransom
OH 3,292,762 2,963,486 4 West Central lMercer
OK 3,274,897 2,947,407 4 Panhandle Guymon
OR 70,439 63,395.] 4 North West Yamhill
PA 1,541,633 1,387,470 4 South East Lebanon
SC 538,219 484,397.] 1 South Central Orangeburg
SD 2,324,800 2,092,320 4 South East Sioux fall
TN 670,236 603,212.4 4 West Fayette
TX 921,404 829,263.6 4 North H. Plains Fort Worth
UT 280,720 252,648 4 South East Orangeville
VA 590,142 531,127.8 1 Central Toga
WA 55,652 50.0868 4 East Central Grant
WV 29,587 26,6283 4 Western Charleston
WI 1,576,287 14,18658 4 South West Grant
WY 250,887 225,798.3 4 South East Cheyenne
New England 46,895 42,205 .5 3 North East Laconia
AK & HI 28,784 25,905.6
Total (U.S.) 104,302,165 93,871.94?“

 

 

 

 

 

 

 

120

 

7.3.2 Processing regions

All the pork-processing plants that were operational in 1997 are considered to be

processing regions. If a single state has two or more processing facilities, they are

combined to represent one processing region. The existing capacities of the plants are

assumed to be the maximum capacities of processing (Table 7.2).

Table 7.2 Annual maximum hog slaughtering capacity in different regions (1997)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

lRegion Capacity Processing cost Location of plantsM
Arkansas 351.000 26.07 Little Rock
California 1.872,000 25.65 Nemon
Iowa 30,667,000 25.54 Waterloo
lldaho 169,000 25.25 Twin Falls
llinois 8,502,000 25.08 Beards Town
ndiana 7,280,000 25.91 ogansport
Kansas 4 1 6000 25 .62 Downs
entucky 2,145,000 25 .33 Louisville
Minnesota 8,242,000 26. l 7 Austin
[Missouri 4,368,000 24.38 Marshall
[Mississijpi 1,690,000 23.74 West Point
N. Carolina 8,320,000 24.54 ar Heel
N. Dakota 239,200 24.96 Minot
Nebraska 7,] 50,000 25 .5 Fremont“
Ohio 962,000 28.13 Sandusky
Oklahoma 2,080,000 25.26 Cuymon“
Oregon 143,000 26.5 Klamath Falls
Pennsylvania 2,028,000 26.59 Hartfield
S. Carolina 780,000 24.91 Green Wood
S. Dakota 3,900,000 25.5 Sioux Falls*
Tennessee 520,000 25.13 New Burn
Texas 208,000 25.] Richardson
Virginia 4,758,000 25.86 Smithfield
Wisconsin 650,000 27.21 Water Town
Total (U.S.) 97,440,200

 

 

 

 

 

*Cost estimates in these locations are based on the regional average.
”All the processing plants in individual states are combined as single plant location.
”*Details per unit processing costs calculations are discussed in Chapter Six.

121

 

It is not likely that all the processing plants will operate everyday during the year. For

simplicity we can assume that a processing plant’s maximum annual capacity cannot

exceed 260 multiples (i.e. 52 weeks of five working days) of existing daily capacity. The

value of by-products such as organs, bones, skin and hair that are obtained from

processing should be taken into account in order to calculate the cost of pork production.

This issue is discussed in section 8.3 and processing costs are discussed more in Chapter

Five.

7.3.3 Pork consumption regions (markets)

Table 7.3 Regional demarcation and quantity of pork demanded (1,000 lbs)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

State Demand point (Node) Demand State Demand point (N ode) Demand
AL IMontgomery, AL 230,323 NE Lincoln, NE 91,721
AR Little Rock, AR 179,349 NV Las Vegas, NV 46,352
AZ Phoenix, AZ 134,560 NJ Trenton, NJ 306,703
CA Fresno, CA 1,272,857 NM Santa Fe, NM 68.070
CO Denver, CO 153,737 NY New York, NY 690,892
CT Hartford. CT 124,465 NC Raleigh. NC 396,037
DC Washing. DC 39,186 ND Bismarck, ND 35,035
DE Dover, DE 28,189 OH Columbus, OH 613,772
FL Orlando, FL 732,799 OK Oklah. City, OK 176,690
GA Atlanta, GA 399,099 OR Portland, OR 123,134
ID Boise, ID 47,830 PA Philadelphia, PA 457,565
IL Chicago, IL 657,510 RI Providence, RI 37,584
IN Indianapolis, IN 321,454 SC Columbia, SC 202,056
IA Des Moines, IA 156,250 SD Pierre, SD 40,007
KS Kansas City, KS 139,432 TN Nashville, TN 286,735
KY Lexington, KY 208,333 TX Fort Worth, TX 1.031,877
LA Alexandria, LA 231,93] UT Salt L. City, UT 81,600
ME "gum ME 47,418 VA Richmond, VA 22,416
MD Annapolis, MD 271.513 vr ontpelier. VT 358,943
MA Boston» MA 232,877 WA Olympia. WA 221,407
MI Detroit, MI 535,656 WI Milwaukee, WI 96,793
MN St. Paul, MN 256,606 WV Charleston, WV 284,661
MS Columbus, MS 145,639 WY Cheney, WY 18,965
MO Columbia, MO 288,264 Export, l-II, AK 784,355
MT Billings, MT 34,716 Total 12,746,500
NH oncord, NH 63,062

 

122

 

Demand for pork consumption has been estimated in Chapter Four. For
mathematical programming purposes, the contiguous U.S. is divided into the 50
consumption regions (Table 7.3). Mostly the state capitals or the major metropolitan
cities are assumed to be consumption centers. Processed pork is distributed to the
consumption regions at wholesale levels. Retail distributions to the local outlets are not
included in the model.

7.3.4 Transportation cost

Transportation cost is one of the important components in an interregional
competition model. Transportation costs influence the magnitude of flow of the
commodity. The gains from the regional flow of commodity can accrue only if there is
some means to transport goods from one geographical region to another region at a cost
that is less than the difference in market prices between the two regions. The product
movement between regions creates a derived demand'9 for transport services.

It would be desirable to use actual point-to-point transportation rates, but lack of
data hinders this approach. The model assumes a single pickup or delivery point for each
supply and demand region. The trucking rates are the increasing function of mileage, but
the relationship may not be perfectly linear. The shipping of pigs/pork incurs loading and
unloading costs, which is not related to distance between the origin and destination.
Several assumptions, such as that the trucks are in full load, there are no quantity
discounts, and there are no time discounts (faster delivery vs. slower delivery), are made

to make the model simple. Although we recognize the non-linearity property of

 

l9 Demand schedules for inputs that are used to produce final products. The term-derived demand is
applicable to wholesale or fann-level demand functions. Derived demand incurs marketing. processing and
transportation costs (Tomek and Robinson).

123

transportation costs, we assumed a flat rate of transportation cost, i.e. five cents/cwt per
mile. This rate is consistent with the census bureau data and with expert opinions.
Highway distance between point of origin and destination was estimated using the
network analysis procedure of the geographic information system (GIS). Mostly the state
capitals or the major metropolitan cities are assumed to be consumption centers. Costs of
pork distribution from consumption centers (wholesale) to the supermarkets in local cities
and towns are not accounted for in this analysis. The analysis would be too complicated if

we were to consider all the cities and towns in the distribution network.

7.4 Transshipment model tableau

A simple two-region programming tableau, which is consistent with the equations
7.1 through 7.5, is given in Table 7.4. The tableau shows the flow of a commodity
through production, processing and marketing activities. Production activities in regions
A and B are given in the first four columns. Columns 5 through 8 are transportation
activities in which pigs are transported to processing plants in region A and B. The
technical coefficients (Cj) and transfer coefficients are given in the body of the tableau. Bi
represents the regional restrictions on production capacities, processing plants capacities,
and regional demands that need to be met. The coefficient 0.61 indicates the conversion
factor for converting live hogs to the pork cuts for wholesale. In other words, out of 100
pounds of live pigs, we recover only 61 pounds of pork (based on expert opinion). The
remaining 39 percent of weight goes to by-products (hide, lard, hotdogs, etc.) and wastes.

The coefficient of 0.1 is the conversion factor from 1,000 cwt to million pounds.

124

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125

A simple two-region transshipment model was extended to find optimal
production, processing and flow of pigs and pork in the U.S. The extended model
consisted of 41 production regions, 24 processing regions, and 50 consumption regions
(markets). The states of Hawaii and Alaska were not included in this analysis. The states
of Maryland, Delaware and New Jersey were combined and assigned as the Maryland
(Baltimore) production region. Similarly, smaller states (ME, NH, VT, MA, RI, and CT)
in the Northeast region were combined and assigned as the New Hampshire (Laconia)
production region. In 1997, only 24 states had pork—processing facilities. If a single state
had more than one pork-processing facility in different locations then they were
combined to make one processing region. All the U.S. states except Hawaii and Alaska
were used as pork markets. Demand for export was treated as a separate production
region. The linear programming algorithms procedure from the General Algebraic
Modeling System (GAMS) was used to program and solve the model. The detailed

GAMS programming model is listed in Appendix 7.]

126

Chapter 8

VIII. Results and discussion

The specific objective of the regional allocation model was to find a set of optimal levels
of regional production, processing, and flow of live pigs and processed pork at a
minimum cost under the given economic environment. The linear programming model
developed to find the optimum solution is shown in Appendix 7.1. In the beginning of
the analysis, the ‘single price’ of the pork in all markets (the national average) was
assumed to estimate the demand of pork (Chapter Four). In the optimal solution of the
transshipment model, the shadow prices of pork were different in various markets. These
shadow prices were used to re-estimate the regional pork demands. Re—estimated
demands (quantity) were entered into the programming tableau. This procedure was
repeated until the model returned stable results (when the sum of the absolute differences
between market prices and the shadow prices converged). The results showed that the
total cost of supplying pork (at the wholesale level) to meet the market 1997 pork

demand was $15,429.34 million.

8.1 Optimum production level by region

The number of pigs marketed (production capacity) in the year 1997 and the
optimum level of pigs (in small-, medium- and large-sized operations) that the production
regions should produce in order to minimize the total cost is listed in Table 8.1. It is
interesting to note that the state of Florida and the New England states have zero
production levels in the optimum solution. The reason behind it is simple: other
production regions can produce and ship pigs at lower costs instead of producing pork in

these regions. Large-sized operations in most of the production regions should produce

127

at current levels to meet the market demand. Small- and mid-sized operations are not
competitive in some states/regions. Higher cost of production in small-sized operations
makes them less competitive compared to the large-sized operations. The production
regions, which have zero production at the optimum level, have the highest shadow price
(zero instead of a negative number). The shadow price of —103.24 in the state of
California (Appendix 8.1), for instance, indicates that if one can manage to market one
more finished pig from a large-sized operation in California, the total cost (the objective
value) would decrease by $103.24. Additional production of hogs in the production
region where there is already a surplus (slack) production, does not contribute in cost
minimization and therefore have a “zero’ shadow price. In other words, a shadow price
may be described as the value of resources in a particular production region, i.e. the
amount to be compensated to the producers (Appendix 8.1).

The shadow price of production ranges from $ —122. 1 5 per hog (Nevada, large-
sized operation) to $0.00 (FL and New England). The states of Nevada, California,
Oregon, New York, Missouri and South Dakota have higher negative shadow prices.
Raising hogs in these regions reduces the total cost (the objective function) more quickly
than in the production regions with lower negative shadow prices. If other conditions
remained the same, these states should be considered if pork production were to be
expanded. The current production level of hogs in these states is limited and it is costly
to transport pork from the Corn Belt states to fulfill the demands. The total welfare of the
country would improve by producing more hogs in these areas instead of transporting

pork. The total number of slaughter hogs sold (capacity) in various regions and level of

128

production in solution by various sizes of operations is presented in Table 8.1 and

Appendix 8.1.

Table 8.1 Regional allocation of production by size of operations (1,000 of pigs)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

, Operation size and level of production Highest
[Productron Upper Shadow
region Reference point Small Medium Large Total limit Slack Price 3
AL Jackson 20 75 191 286 341 55 0
AR De Queen 0 435 436 871 1,014 143 0
VtZ Navajo 78 78 199 355 355 0 -l.45
CA Bakersfield 72 72 184 328 328 0 -59.90
CO Morgan 0 0 446 446 1,344 898 0
FL Gainesville 0 0 0 0 103 103 0
GA Albany 0 0 436 436 990 554 0
IA Des Moines 6,444 9,191 5,493 21,128 21,128 0 -3.89
1D lewiston O 15 38 53 68 15 0
1L Henry 2,384 3,035 24 5.444 5,444 0 -22.86
IN Anderson 1,861 2,521 1,621 6,003 6,003 O -2.61
KS Stevens 0 0 79 79 2,942 2,863 0
KY Davies 337 3 88 296 1,022 1,022 0 -20.33
LA Alexandria 0 13 32 45 58 13 0
MD Baltimore 41 41 103 184 184 0 - l 3.56

l Kalamazoo 0 670 468 1,138 1,559 421 0
WIN Martin 2.428 3,237 2,428 8,092 8,092 0 -9.69
MO Chariton 1,260 1.375 3,094 5,729 5,729 0 -27.82
MS Columbia 0 90 230 320 410 90 0
MT Sweet Grass 0 0 19 19 237.6 219 0
NC Bladen 0 2,651 10,610 13.261 14,736 1,475 0
ND Ransom 1 1 64 164 239 293 54 0
NE Columbus 2,304 1,855 1.461 5,621 5,621 0 -18.42
N. England Laconia 0 0 0 0 42 42 0
NM Albuquerque 0 2 5 7 9 2 0
NV Sparks 4 4 10 18 18 0 -79. 14
NY Genesee 26 26 66 1 18 1 18 0 ' -14.28
0H Mercer 772 1,096 3 56 2,224 2,963 739 0
OK Guymon 0 0 2.417 2.417 2,947 530 0
0R Yamhill 14 14 36 63 63 0 0
PA Lebanon 375 638 375 1,387 1,387 0 -9.91
SC Drangeburg 0 107 271 3 78 484 106 0
SD Sioux Fall 847 534 711 2.092 2,092 0 -23.9
TN Fayette 133 133 338 603 603 0 -23.95
TX - Fort Worth 0 0 208 208 829 621 0
UT Orangeville 0 56 141 197 253 56 0

 

 

 

129

 

 

 

 

 

 

 

 

 

 

 

 

 

 

, Operation size and level of production Highest
Producnon Upper Shadow
region Reference point Small Medium Large Total limit Slack Price $

A & WV Toga 123 123 312 558 558 0 -4.52
WA rant 11 1 1 28 50 50 0 -3.75
w1 Cram 0 539 847 1,386 2,180 794 0

Y Cheyenne 0 0 126 126 226 100 0

 

 

 

Note: Upper limit is the right hand side of the constraint in mathematical programming.
Slack level of production implies unused production capacity. Reference point is the

location where production is concentrated in that particular production region and
distances for transportation were measured from this point.

8.2 Optimum level of pork processing by region

Pork processing plants obtain finished pigs from the production regions. Live pigs

are transported from the surrounding production regions to the processing plants as an

intermediate product. As discussed earlier, processing plants have capacity constraints. It

may not be possible to process all the pigs raised in the processing region due to capacity

constraints of plants. Similarly, some processing plants do not have a sufficient supply of

live hogs and they need to haul pigs from other regions. Table 8.2 indicates the

pattern/direction of live hog flow from production regions (origins) to processing regions

(destinations).

130

Table 8.2 Pattern of pig flow in the optimum solution

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Processing Source of pig Processing region Source of pig
region (Production region/state) (000 Head) (Production
(000 Head)‘ region/state)
AR (351) AR ND (239) ND

CA (1,351) AZ, CA, CO, NV, NM, UT NE (7.150) NE,1A

IA (19,380) IA OH (962) OH

ID (169) ID, MT, WY OK (2,080) OK

1L (6,805) 1L, MO OR (143) OR, ID, WA
1N (7,280) IN, MI, OH PA (2,028) MD. NY, NC, PA
KS (416) KS, OK SC (780) NC, SC

KY (2,145) KY, IN SD (3,198) MN, SD

MN (7,941) IA, MN, W1 TN (520) AR

MO (4,368) MO TX (208) TX

MS (1.690) AL, GA. LA, MS, TN VA (4.758) NC, VA

NC (8,320) NC WI (650) W1

 

 

*Numbers in parentheses indicate the total number of pigs shipped from the
production region(s) to the processing region.

The states of California, Mississippi and Pennsylvania are major live hog deficit

states and they bring live hogs from various other states (production regions) to keep their

pork processing plant running at full capacities. The states of Iowa and North Carolina

are major pork-producing states and they supply live hogs to various processing regions.

Table 8.3 Locations and optimal levels of processing (1,000 of hogs)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Location Total Processing Shadow price“

Region of Processing Processed Capacity Slack $/hog
LAR Little Rock 351 351 0 -88.47
CA Vernon 1350.961 1,872 521.039 0
1A Waterloo 19379.51 30,667 1 1287.49 0
1D Twin Falls 169 169 0 -62.76
1L Beards Town 6804.919 8.502 1697.081 0
IN Logansport 7280 7,280 0 -33.61
KS Downs 416 416 0 -43.1
KY Louisville 2145 2,145 O -40.15
MN Austin 7940.573 8.242 301.427 0
MO arshall 4368 4.368 0 -15.62

S West Point 1690 1,690 0 -60.07
NC Fl‘ar Heel 8320 8.320 0 -57.06
ND Minot 239.2 239 -0.2 -44.16
lNE Fremont 7150 7.150 0 -6.95

H Sandusky 962 962 0 45.15

 

131

 

 

 

 

 

 

 

 

 

 

 

 

 

Location Total Processing Shadow price“
Region of Processing Processed Capacity Slack $/hog
OK Gmon 2080 2.080 0 -90.61
Org Klamath Falls 143 143 0 -36.42
PA Hartfield 2028 2,028 0 -43.88
SC Green Wood 780 780 0 -90.94
SD Sioux Falls 3198.313 3.900 701.687 0
TN ew Burn 520 520 0 -41.01
TX Richardson 208 208 0 -1 15.79
VA Smithfield 4758 4,758 0 -54.98
WI Water Town 650 650 O -38.93
USA 82,931 97 .440 14508.53

 

 

 

 

 

 

 

*Shadow price indicates that additional processing capacity in that particular region
would reduce the objective value by the listed amount.

Current pork-processing capacities (upper bound) of different regions and the optimum
level of processing required to meet the consumer demand are listed in Table 8.3 and
Appendix 8.3. It is interesting to note that most of the processing plants are operating at
full capacities. Processing capacity in many processing regions is a limiting factor, at
least in the short run, to expand the pork industry. Processing plants in Vernon (CA),
Beards Town (IL), Waterloo (IA), Austin (MN), and Sioux Falls (SD) could process
more hogs from the current optimum level if there were more demand for pork for
consumption in US or for export. The processing plants that have slack processing
capacities have “zero” marginal values/shadow prices. Therefore, increasing the
processing capacities in these surplus capacity regions under the given conditions does
not contribute to reduction of the total cost in the system. Regions with the larger
negative shadow prices (e. g. Texas) are the ones where the processing capacities should
be expanded first. In the long run, processing industries adjust their location (immobile

processing plants become mobile) and the processing plants can be shifted to different

132

regions, if it is more profitable to do so. The states of Texas, Oklahoma, South Carolina,
Arkansas, and Missouri will be the top five processing regions for expansion of
processing capacities in the future if the demand of pork grows.

Table 8.4 Shipment of pork from processing regions to the markets

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Market“ Processing Market Processing Market Processing
AL 1L, MS LA AR, NE OH TN
AR AR, TN MA OH, PA OK NE
AZ OK MD NC OR ND, OR, SD
CA CA, MN ME PA PA NC
CO SD M1 1A RI VA
CT NE MN MN SC NC
FL 1L, KY, NC, SC MS MS SD SD
DC NC, VA M0 M0 TN IL
DE NC MT SD TX KS, MO, NE, OK, TX
GA IL NC NC WA SD
[A IA ND ND W1 W1, IA
ID ID, NE NE NE WY NE
IL [A NH PA WV KY
IN IN NM OK UT NE
KS IA, M0 N1 VA VT PA
KY IN, KY NV CA VA VA
NY 1A, PA, VA ExEm 1A

 

 

 

 

 

 

 

*Wholesale markets (destination) obtain processed pork from the processing regions
(origin) to fulfill retail market.

Processing plants supply pork to the wholesale markets. The optimal solution in
Table 8.4 indicates the flow (direction) of pork from processing regions to the markets.
Quantities of pork shipped from the processing regions to the markets are listed in
Appendix 8.2 that would minimize the total cost under the given set of constraints. Pork
processed in Iowa, North Carolina, Nebraska, and Pennsylvania covers most of the
markets. Looking at the Table 8.4, a question can be raised: why Arkansas is shipping
out pork to Louisiana and shipping in some pork from Tennessee. It sounds a little
confusing, but it should be kept in mind that the processing plants and the markets may

not be in the same location in the same state. The distance between processing plants and

133

market and transportation costs along with other constraints determined the direction of

pork shipments.

8.3 Pork demand and shadow prices

In Chapter Four, the demand for pork was estimated for each market. Data on
meat consumption and prices by each region (states) were not available. Therefore, the
national average of per capita of pork consumption was estimated by a system of
equations using the national average quantities of meats and their prices. Their regional
demand for pork was then adjusted on the basis of demographic characteristics and their
pork consumption behavior (details in Chapter Four). The shadow prices in different
markets obtained from a cost minimization procedure were used to re-estimate the pork
demand. This procedure was repeated several times. Total pork demands and the
shadow prices by markets (states) in the optimal solution are listed in the Table 8.5.

In terms of total quantity of pork demand, the top ten markets are CA, TX, FL, IL,
NY, OH, MI, PA, NC, and GA. The shadow price of pork ranged from $1.20 (IA) to
$1.96 (WA) per pound at the wholesale level (shadow price for export is $1 .14/pound but
it is due to the fact that transportation costs involved in export are not included in the
analysis). Markets in WA, OR, ME, and ID in the Western region, and the New England
states in the Northeast region have relatively higher shadow prices. This information
indicates that it is expensive to supply pork to these markets in the current pork industry
settings. This result may be useful to the pork industry leaders. Expansion of pork
production and processing capacities in these areas, where the shadow prices of demands
are higher would reduce the total costs and would ultimately improve the total social

welfare.

134

Table 8.5 Market demand (Mil. Pounds) and shadow prices

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Optimum Shadow Optimum Shadow

Market“ Demand Price Market Demand Price
AL 210.737 1.61 ND 34.825 1.36
AR 127.238 1.50 OH 593.82 1.43
AZ 158.373 1.74 OK 168.922 1.47
CA 1111.101 1.77 ORG 107.027 1.94
CO 146.351 1.48 PA 414.848 1.64
FL 675.829 1.81 SC 183.879 1.63
GA 367.231 1.59 SD 40.874 1.28
IA 164.884 1.20 TN 274.452 1.47
ID 40.878 1.85 TX 955.36 1.57
IL 668.297 1.30 UT 72.916 1.69
IN 319.826 1.35 VA 338.532 1.51
KS 141.662 1.30 WA 184.067 1.96
KY 201.115 1.44 W1 291.42 1.28
LA 208.074 1.67 WY 18.064 ‘ 1.48
MD 250.406 1.58 NH 54.553 1.80
Ml 519.044 1.43 CT 111.288 1.69
MN 265.663 1.25 DC 36.323 1.57
MS 137.293 1.51 DE 26.035 1.58
MO 294.321 1.28 MA 202.95 1.78
MT 32.884 1.50 MB 40.459 1.86
NE 94.422 1.26 NJ 276.588 1.66
NV 40.195 1.79 RI 32.786 1.77
NM 63.892 1.53 VT 19.482 1.79
NY 618.077 1.68 WV 90.805 1.53
NC 371.967 1.52 EX 847.015 1.14

 

 

*Export includes demand from the states of Hawaii and Alaska.

The average price of pork in this model at the wholesale level is $1 .22/lb and the
total pork marketed is 12,647 million pounds. Pigs are slaughtered and processed into
pork cuts by stande ways at the packing plants, to sell in the wholesale market.
Wholesale cuts are further processed for retail sale. During these processes, in addition to
meat (pork), a number of by-products are obtained which have economic value. The

value of the by-products must be taken into account while calculating pork price spreads.

135

An USDA report20 indicates that the average value of by-products account for $0.05 per
pound of pork at the wholesale level. With this piece of information, we can adjust the
wholesale price. The prices of by-products were subtracted from the total processing
costs so that the imputed pork price would take into account the by-products. According
to industry experts, after adjusting for by-products, the average retail price of pork would
be about a 75 —100 percent mark-up from wholesale prices. If we assume the given

mark-ups, then the estimated retail price of pork would be $2.13 to $2.44 per pound.

8.4 Industry implications

The analysis of the pork sector discussed in this study would be useful to the U.S.
pork industry participants. The analysis contains useful information about the
competitiveness of the various regions/states in pork production and processing. Some of
the existing pork production operations (particularly the smaller-sized operations) are not
efficient and therefore, will exit the industry. Small-sized production facilities are
vulnerable and the trend of fewer and larger hog operations will continue.

The cost minimization model used in this study indicates that the states of Florida
and New Hampshire (representing the New England States) should not raise pigs at all.
However, in reality this statement may not be practical. This can be taken as an
indication that pork production in these areas is less likely to expand under the economic
environment outlined in the model description in Chapter Seven. Higher Production
costs and distant processing facilities make the pork production expensive in these

regions.

 

20 http://www.ers.usda.gov/briefing/foodpricespreads/meatpricespreads/pork.xls

136

Higher negative shadow prices (marginal costs) in the states of NV, CA, OR, NY,
MO and SD (for example) are an indication that the pork industry would be better off to
expand production in these regions. Demands of pork relative to supplies are higher in
the states with higher negative shadow prices. Human settlement and feed availability
are probably the most important factors for pork industry structure. Feed cost is a major
cost component in production and it is expensive to transport pork if the distance between
production regions and markets is too far. Expansion of pork production and processing
capacities in the areas (CA, TX, FL, IL, NY, OH, MI, PA, NC and GA), where the
shadow prices of pork demands are higher (negative) would reduce the total costs.
However, production and processing costs are also important consideration to decide the
pork production locations. The states of Florida and Georgia have slack live hog
production on the supply side and higher shadow prices on the demand side. The
processing facility is the one of the limiting factors here. Establishment of processing
facilities in these states would save the transportation cost. In the current (year 1997)
pork industry setting, the costs of supplying pork in the Western and Northeast regions
are higher. If the pork industry expands its production and processing facilities in these
regions, the first mover is likely to reap good incentives.

This study made several assumptions in pork demand analyses, cost of production
and processing analyses, and linear programming modeling. The linear programming
model requires the assumption that the parameters and constant values in the model are
known with certainty. The model requires specifically defined values to represent pork
demand, production costs, environmental compliance costs, processing costs, technical

coefficients described in Table 7.4 (programming tableau), capacity constraints, and

137

transportation costs. All these parameters were estimated or compiled using the
secondary data obtained from different sources (Appendix 1). Due to the uncertainty of
future events and quality of the data used, there is a potentiality of significant deviations
between the parameters used in this analysis and the real parameters. Therefore, analysis

of a likely future scenario would be useful.

8.5 Scenarios analysis

It is important to conduct sensitivity analyses in order to determine the robustness
of the results of the mathematical programming modeling. One may ask a question: what
would happen if one or more assumptions were relaxed or changed? Sensitivity analyses
would be useful to visualize the impact of likely scenarios in the pork industry. The
impacts of a few likely scenarios on the base model (model described above) are
analyzed below. The scenario differs from the base model by these following factors:

1. Increase in pork demand

2. Expansion of pork production

3. Expansion of pork processing capacities

4. Increase in regulatory compliance cost
8.5.1 Increase in pork demand

Per capita pork consumption in the U.S. does not show any trend by time.
Increase in population size is the most important factor in the quantity of pork demand.
The U.S. Census Bureau has projected population by states based on assumptions about
future births, deaths, international migration, and domestic migration. Population
projections are available for the year 2005, 2015 and 2025. The U.S. population by states

for 2010 was linearly extrapolated between 2005 and 2015. The projected U.S.

138

population would grow by 12 % from the 1997 population. If the per capita pork
consumption in 2010 remained at current levels then the total pork demands by state
would change by the proportionate change in population. If this assumption holds, there
would be a higher growth of pork demand in the Western states (e. g. Nevada, Colorado,
Washington, and Utah) and grth would be slower in the Corn Belt states and the
currently highly populated areas (Table 8.6).

As discussed earlier (Chapter Four), U.S. pork export increased by 250 percent
from 1989 to 1997. Asia is considered to be an important export market for the U.S. pork
industry. Canada, Australia, European Union, and Latin America are other important
markets for U.S. pork export. It is expected in the near future that the export demand of
pork will grow dramatically. If the trend continues, an USDA projection shows that total
pork export in the next decade will be approximately double the 1997 level of pork
export. In this scenario, total pork export would be 1,426 million pounds in 2005. Let us
assume that this level of export will hold in the year 2010 too.

8.5.2 Expansion of production

In recent past decades, the number of hog-raising farms has dropped sharply
(Table 3.1), however the total number of farms keeping more than 1,000 pigs has
increased. Smaller farms are continuously leaving the hog business. It is expected that
this trend will continue in the future and the hog industry will be further geographically
concentrated. According to an industry expert (personal interview), pork-producing
states are classified into four groups. The classification is given in Table 7.1. Assume
that pork production will expand first in class four production regions “likely to expand”.

When the production expands it would follow the historical trend and there would be

139

 

growth in medium- to large-sized operations and small-sized Operations would continue
to disappear. Let us further assume the number of pigs raised by medium- to large-sized
operations would double and small-sized operations would remain the same in the pork
production regions that are identified as “likely to expand” regions.
8.5.3 Expansion of processing capacity in the West

Pork processing capacity seems to be a limiting factor in most of the regions. In
the current industry structure, there are few processing facilities in the western region of
the U.S. From the base model, we observed that pork in the Western states was relatively
expensive (high shadow price). Results show higher negative shadow prices in the states
of Nevada, California, and Oregon. Higher shadow price comes partly from the higher
transportation costs which could be reduced if there were more processing facilities in the
region. If the trend of location shift continues, it is likely that the production and
processing of pork will expand toward the West. In the year 2010, let us assume pork-
processing capacity in the West would double from the current level (1997).
8.5.4 Increase in compliance costs

The compliance cost and industry location is a much-discussed topic in pork
industry related literature. Industry experts and scholars believe that regional variations
in environmental regulations influence migration of hog/pork operations to the locations
where the regulations are less severe. In Chapter Six, environmental compliance costs by
production regions and size of operations were estimated (Table 6.6). These estimated
costs were incorporated into the total cost of production. The estimated environmental
costs did not have a large share in total costs (roughly one percent of total costs).

Metcalfe (2000), in a study, also concluded that environmental costs have minor impacts

140

on the price of pork. In his study, increases in environmental compliance costs by 25

percent to 200 percent lead to a 0.26 percent to 2.05 percent decrease in pork export. It

implies that compliance costs do not affect the competitiveness of the hog industry.

However, governmental regulations are uncertain and difficult to predict. We know from

the environmental stringency grouping in Table 6.3 that some U.S. states are more

stringent than others. Let us assume that compliance cost will increase sharply (say

double from the year 1997 level) in “Highly Restrictive” and “Restrictive” states (KY,

NE, OH, IL, NC, SD, OK, SC, MD, CA, ND, UT, VA, WI, WY, FL, IN, MN, VT, CT,

IA, MO, MS, AR, KS, TN, TX) and that it is not changed in other less stringent states

(NY, WA, NV, AZ, ID, NM, MT, OR, PA, RI, AL, NJ, C 0, ME, MI).

8.5.5 Results of the scenario analysis

Results of the base model showed that the states of Florida and New Hampshire

(New England) have no production in the optimum solution. The new projected scenario

(Year 2010) also now has the states of Washington, Colorado and Louisiana out of the

production regions. Most of the small-sized operations (e.g. AL, FL, GA, IN)

Table 8.6 Optimum level of pork production in year 2010 (1,000 of pigs)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Size of Level in Shadow Size of Level in Shadow
figion Firm Solution“ Slack Price $/pig Region Firm Solution Slack Price S/piL
AL Small 0 149.904 0 MT Large 266.02 0 -1.65
Medium 0 149.904 0 N.Eng. Small 1.956 0 -36.415
Large 381.573 0 -235 Medium 1.956 0 ' -61.725
AR Small 0 78.195 0 Large 4.977 0 -74.415
Medium 0 156.389 0 NV Small 64.36 0 -58.3
Large 398.077 0 4.375 Medium 128.72 0 -87.57
AZ Small 0 111.5 0 Large 327.652 0 -101.31
Medium 932.549 0 -10.01 NM Small 0 294.721 0
Large 871.731 0 -23.75 Medium 0 7662.758 0
CA Small 72.097 0 -38.745 Large 2703 .22 18516.73 0
Medium 144.194 0 -67.815 NY Small 575.892 935.486 0
Large 367.042 0 -81.355 Medium 1096.49 0 -18.9

 

 

141

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Size of Level in Shadow Size of Level in Shadow
Region Firm Solution“ Slack Price $/pi Region Firm Solution Slack Price $/pig
CO Small 0 295.61 1 0 Large 355.618 0 -29.3
Medium 0 295.61 1 0 NC Small 2304.486 0 -24.505
LaLge 0 752.465 0 Medium 1854.831 0 -46.275
FL Small 0 22.767 0 Large 1461.381 0 -56.625
Medium 45.535 0 -5.615 ND Small 3.938 0 -58.88
Large 1 15.906 0 -16.015 Medium 7.877 0 -84.63
GA Small 0 227.716 0 Large 20.048 0 -97.58
Medium 653.447 0 -0.02 OH Small 0 176.845 0
Large 871.261 0 -12.51 Medium 0 707.378 0
1A Small 2384.435 0 -21.885 Large 4833 .749 0 -9.78
Medium 6069.47 0 -47.185 OK Small 0 13.947 0
Large 48.78 0 ~59.885 Medium 27.895 0 -17.555
ID Small 0 15 .004 0 Large 71.003 0 -27.925
Medium 0 30.008 0 OR Small 0 374.617 0
Large 76.385 0 -5.365 Medium 0 1276.472 0
IL Small 1861.041 0 -1 1.46 Large 429 320.234 0
Medium 5042.819 0 -31.93 PA Small 106.567 0 -6.365
Large 3241 .813 0 -44.45 Medium 213.134 0 -25.245
IN Small 0 6444.004 0 Large 542.525 0 -35.595
Medium 12096.8 6284.459 0 SC Small 847.39 0 -19.625
Large 10986.5 0 -12.52 Medium 533.542 0 -40.915
KS Small 0 735.594 0 Large 71 1.389 0 -52.545
Medium 1353.494 0 -1.475 SD Small 132.707 0 -33.015
Large 3060.072 0 -14.185 Medium 265.414 0 -58. 135
KY Small 0 337.17 0 Large 675.598 0 -70.805
Medium 776.51 1 0 -20.59 TN Small 0 182.438 0
Large 592.601 0 -30.94 Medium 364.876 0 -4.31
LA Small 0 12.678 0 Large 928.775 0 -14.91
Medium 0 25.357 0 TX Small 0 55.582 0
Large 0 64.543 0 Medium 0 l 1 1.164 0
MD Small 40.5 0 -38.58 TX Large 208 74.965 0
Medium 81 0 -60.32 UT Small 0 122.706 0
Large 206.181 0 -7 0.7 Medium 122.706 0 -2.025
MI Small 0 420.916 0 Large 312.344 0 -15.525
Medium 670.348 0 -8.435 VA Small 1 1.019 0 -51.6
Large 467.684 0 ~21 . 1 75 Medium 22.037 0 -72.76
MN Small 2427.565 0 -19.68 Large 56.097 0 -84.42
Medium 6473.506 0 -40.06 WA Small 0 794.449 0
Large 4855.129 0 -52.6 Medium 0 1078.18 0
MS Small 0 1260.459 0 Large 0 1693.039 0
Medium 0 2750.092 0 W1 Small 49.676 0 -15.605
Large 1781.35 4406.359 0 Medium 99.351 0 -35.805
MO Small 90.296 0 -40.965 Large 252.895 0 -48.255

 

142

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Size of Level in Shadow Size of Level in Shadow
LEgion Firm Solution" Slack Price $/pig Region Firm Solution Slack Price $/pig
Medium 180.592 0 -60.305 WY Small 0 9.285 0
Large 459.688 0 -72.785 Medium 0 18.571 0
MT Small 0 52.254 0 Large 47.27 0 -7.73
MT Medium 0 104.508 0 NE 7150

 

*Level in solution in thousand of pigs

of the mid-sized operations (AL, AR, ID, MS, MT, OH, OR, TX and WY) and will not

be competitive in pork production by the year 2010. The shadow price of production

ranged from $ -122.15 per hog (Nevada, large-sized operation) to $0.00 (FL, CO, MT,

and WY) in the base model. This range narrowed in the projected scenario ($-101.31 to

$0.00). Details of the size of the firm and underlying shadow prices of production are

listed in Appendix 8.4 (results of scenario analysis).

Table 8.8 Pattern of hog flow in year 2010 (predicted)

Processing region

AR
CA
IA
ID
IL
TN
KS
KY
MN
MO
MS
NC

Source of hog

AR

IA
UT

OK

NV UT

MO

Processing region

ND
NE
OH
OK
OR
PA

SC

SD
TN
TX
VA
WI

Source of hog

ND

NE

OH, Ml

OK

0 ID WA
NY PA
NC SC
MN SD
AR

TX

V

WI

MD

 

In the projected scenario, the pattern of pig flow is similar to the base model. There are

few variations in the pattern. For example, the state of Nebraska shipped in live hogs in

the base model but in the projected scenario, NE obtained live hogs from itself.

Similarly, unlike in the base model, the Pennsylvania processing region did not in-ship

pigs from Maryland, North Carolina and New Hampshire.

143

 

The production level in solution of the the base model (Year 1997) and the

projected scenario (Year 2010) are listed in Appendix 8.4 to identify the winners and

losers. The results show that some of the states gain in pork production share and others

lose from the current optimum level. The state of F L, N.England, NM, KS, and NV will

be top winner in terms of percentage change. Similarly, the states of WA, LA, OK, MO,

and ND will be the top loser in percentage change in production. Increase in the numbers

of hogs slaughtered in 2010 will be substantially higher in the state of IN, MN, IL, and

KS. States of IA, NC, MO, and OK will be in the column of loser by the year 2010. The

result indicates that although the trend of shifting location will be continuous but pork

production will still be concentrated in the Corn Belt states.

Table 8.9 Locations and levels of processing in the year 2010 (1,000 of Hogs)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Shadow Shadow
Region Level Slack Price $/hog Region Level Slack Price $/hoL

AR 351 0 -114.16 NC 8320 0 -28.43
CA 5616 0 —31.14 ND 297.883 180.517 0.00
1A 19368.29 1 1298.71 0.00 OH 962 0 -86.23
ID 507 0 -70.64 OK 2080 0 -79.07
IL 8502 O -39.76 OR 429 0 -103.61
TN 7280 O -74.31 PA 2028 0 -76.46
KS 416 0 -44.19 SC 780 0 -95.40
KY 2145 0 -80.85 SD 7800 0 -4.42
MN 7029.919 1212.081 0.00 TN 520 0 -66.70
MS 1690 0 -107.48 TX 208 0 -130.98
MO 4368 0 -18.40 VA 4758 0 -26.35
NE 7150 0 -4.96 WI 650 0 -33.06

 

 

 

 

 

 

 

 

144

Table 8.10 Pattern of pork flow in optimum solution (Year 2010)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Market Processing Market Processing (origin) Market Processing (origin)
(origin)
AL MS, MO LA SD OH IN
AR AR, IL MA OH, PA OK SD
AZ OK MD NC, VA OR ND, OR, SD
CA CA, MN ME PA PA NC
CO SD MI IA RI PA
CT NE MN 1A, MN SC NC, SC
FL KY, MN MS MS SD SD
DC NC M0 M0 TN IL
DE NC MT SD TX IA, KS, MO, NE, OK, SD, TX
GA IL NC NC WA SD
IA 1A ND ND WI IA
ID ID, NE NE NE WY NE
IL 1A NH PA WV IN, KY
IN IA, IN NM OK UT NE
KS 1A NJ IA VT PA
KY IN NV CA VA VA
Export 1A

 

 

 

 

 

 

The processing capacity in the 2010 scenario is mostly used up. In the base

model, the slack capacity was 15 million head, whereas in the projected scenario the

processing plants except in CA, IA, and SD were completely used up. If the pork industry

required slaughtering about five million more pigs/year, the model would have been

infeasible. Since all of the processing facilities in the base model were kept operational

in the new scenario, the pattern of pork flow was almost identical in terms of direction of

flow (Table 8.10).

145

 

Table 8.11 Demands (Mil. Pounds) and shadow prices (per/lb) in year 2010

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Level Shadow Level Shadow
Market (Mil lbs) Price 8% Market (Mil lbsL Price $/lb

AL 226.11 1.72 ND 36.28 1.46
AR 137.11 1.64 OH 586.52 1.55
AZ 186.50 1.85 OK 179.28 1.58
CA 1283.68 1.84 OR 122.55 2.03
CO 169.61 1.58 PA 412.33 1.76
FL 776.17 1.93 SC 196.80 1.75
GA 417.24 1.71 SD 44.53 1.38
IA 163.63 1.32 TN 303.01 1.58
[D 52.76 1.82 TX 1093.77 1.68

[L 668.07 1.42 UT 87.04 1.79
IN 329.98 1.47 VA 369.14 1.63
KS 148.47 1.40 WA 213.61 2.05
KY 206.29 1.56 WI 299.63 1.40
LA 218.77 1.76 WY 21.98 1.59
MD 268.69 1.70 NH 42.35 1.92
MI 501.82 1.55 CT 112.72 1.79
MN 284.12 1.32 DC 26.75 1.69
MS 144.12 1.63 DE 38.75 1.70
MO 306.85 1.39 MA 207.08 1.90
MT 37.66 1.59 ME 41.71 1.98
NE 97.79 1.37 NJ 287.29 1.78
NV 71.49 1.86 RI 33.49 1.89
NM 77.43 1.63 VT 20.84 1.91
NY 612.18 1.80 WV 89.25 1.64
NC 41 1.67 1.64 EX 1556.64 1.26

 

 

 

*Export (EX) includes demand from the states of Hawaii and Alaska.

The state of CA, FL, TX, IL, NY, OH, MI, GA, NC, and PA are still the top 10 markets
in terms of quantity of pork demanded. The range of shadow price per pound of pork in
the 2010 scenario was $1.06 (IA) to $1.81. The average wholesale pork price went down

from $1.22/lb to $1.19/lb.

146

Chapter 9
IX. Summary and conclusion
The pork industry structure in the U.S. is in rapid transition due to the technology-
induced industrialization process. Technological advances have resulted in cost

efficiency by reducing the average cost of production. However, all the market

 

participants cannot capture benefits by cost efficiency. Large-scale hog production that P:
utilizes new technologies have a competitive advantage over smaller and traditional

operations to capture their market shares. Larger and newer operations have better

arrangements with feed mills, packers, and other contractors to reduce the production t.

costs. This phenomenon encourages a shift to larger and specialized operations. Pork
operations in the U.S. are not only getting larger, but, are also moving to non-traditional
pork producing areas such as Oklahoma, Arkansas and Utah. This structural change
affects positively or negatively on the farm communities, the environment, and
consumers.

The primary objective of this study was to analyze the trends in the U.S. pork
production industry and to review the factors that contributed to structural changes of the
industry. Results of this analysis are useful to policy makers and the pork industry
leadership to introduce to the existing pork industry and to anticipate further changes in
the future.

Structural adjustment in the pork industry is driven by technological changes.
The cost-saving motive in production, processing and distribution of pork is the leading
factor for the development and adoption of new technologies. Public policies and

regulations, property values, alternative economic options, geological characteristics,

147

consumer demand, contractual arrangement, and agglomeration have been described in
the literature as the factors responsible for location change in the hog industry. Among
these factors, consumer demand for pork, environmental regulations, and cost of
production were analyzed in detail in this study.

The almost ideal demand system (AIDS) and the “Rotterdam” models are
common in demand analyses. Based on the recommendation by Alston and Chalfant

(1993), and a better fit on U.S. meat data, the Rotterdam model was chosen to estimate

- -: i 1..qu

per capita meat demand in the U.S. Regional pork demands were then adjusted by the

Tm All: are-'1

demographic compositions and consumption behavior. Per capita pork consumption was
highest in the Midwest, followed by the South, and the lowest pork consumption was in
the Northeast region of the U.S.

An enterprise budgeting approach was adopted to calculate the production cost in
feeder-to-finishing operations. Costs were calculated individually for various production
regions (E. Corn Belt, W. Corn Belt, South, Northeast and South) and type of operations
(small-, medium- and large-sized). Regional production costs were then adjusted by
states for variations in input prices (e. g. corn price, soybean price, wage rates etc.).
Environmental compliance costs were also incorporated into the enterprise budgets. The
result of cost analysis suggests that smaller operations have limited ability to compete
with larger production facilities. Larger operations have higher efficiencies in feed,
buildings and equipment and labor. As is with the number of farms raising hogs, the
number of pork processing facilities has declined. The number of pork processing firms
were 446 in 1980 and the number declined to 209 in 1995. There are a few dominant

meatpacking plants (Smithfield, IBP, ConAgra, Cargil and Hormel) that process more

148

than 60 percent of total pork supplied in the U.S. markets. The competitive advantage of
one production region over other regions arises from the various factors such as lower
feed costs, higher feed efficiency, friendly environmental and other regulations, and
accessibility of markets.

A mathematical programming method (transshipment linear programming), as
suggested by Takayama and Judge (1971) was used to analyze the interregional
production, processing and distribution of pork in the U.S. to minimize the total cost in
the system. Forty production regions, three types of production units (small, medium and
large), 24 processing regions and 50 markets were used in the LP model. The results
revealed that existing pork production operations in the Florida, and New England
production regions are not competitive due to higher production costs and distant
processing facilities. The states of NV, CA, OR, NY, MO and SD have higher negative
shadow prices in the mathematical programming solution. It is an indication that these
production areas should be considered for expansion of pork production in order to
minimize the total cost in the system. The results also revealed that the pork industry
tends to locate near the populous areas due to easy market access and to reduce the
transportation costs. But, the opposite forces- threat from current and future environment
regulations, scarcity of agricultural land for manure management and the government
moratoria tend to keep the hog operations away from major cities and towns. These
opposite forces along with other factors determine the locations of pork production. A
likely future scenario analysis suggested that the Westem region would experience higher

growth in pork production compared to other regions by the year 2010. The trend of

149

smaller production units leaving the industry will continue and the pork industry will be

more consolidated in fewer and larger operations.

Limitation of the study:

1.

This study relied on the secondary data from different sources (Appendix 1).
Some of the key data were obtained from expert opinions. Results of the study
are greatly affected by the quality of the data. Some of the data were not
available due to disclosure reasons.

In the mathematical programming section, only the price of the pork was allowed
to change in the iterative procedure to adjust the market demand. Prices of other

meats were kept unchanged. The substitution effect was ignored.

. Regional demarcation of production, processing and markets were broad (state

level). The model estimated the state level aggregate supply and demand .
Expanding the model up to townships and city level would generate better results,
but such expansion would be costly in terms of time and money.

Export demands were treated exogenously and analysis of the export market
would better predict the pork industry in future.

This model doesn’t cover many aspects (factors such as quality of meat, land
values etc.) due to the unavailability of data. There is the potentiality of

introducing errors.

150

 

Suggested future research:

1.

.Id

The cost minimization model presented in this study allocated the optimal level of
pork production to each production regions. Most of the production regions are
individual states. The optimal level of production of hogs in Michigan, for
instance, was 1,138,000 pigs per year. Although production is scattered
throughout the state, it is more concentrated in the Kalamazoo area. It would be
interesting to discover the best locations in Michigan that would meet all the
constraints. Application of the geographical information system (GIS) would be
highly desirable at this point. Factors such as geological characteristics (soil type,
soil fertility, topography and hydrology), locations of cities and town, rivers,
lakes, parks and roads along with federal, state and local regulations and standards
(e.g. setbacks) could be considered in the analysis. Such analyses would be useful
for all production regions (states) to identify exact locations of hog production.
Processing capacity is one of the major bottlenecks in the pork industry expansion
in the U.S. It is expected that there would be higher population growth in the
western part of the country. Future expansion of the pork industry would be in
the Western region due to the higher pork demand and availability of agricultural
lands. It would be useful to do feasibility studies of establishing one or more meat

packing plants in the West (e. g. Washington, Idaho and Utah).

151

 

Appendices

Appendix 1: Source of data

 

Data

Source

 

Price of corn

USDA-NASS Agricultural prices, Annual Summary

 

Price of hogs

USDA-NASS Agricultural prices, Annual Summary

 

Hog inventories

US Census of Agriculture

 

No. of farms by states

US Census of Agriculture

 

Population

US Bureau of the Census

 

Meat prices

USDA-NASS Red Meat Yearbook

 

Pork consumption by region

USDA Continuing Survey of Food Intakes by Individuals

 

Per capita income

Income Statistics Branch, US Bureau of the Census

 

 

 

Wage rates Bureau of Labor Statistics
Opportunity costs of labor USDA, Technical Bulletin number 1848
Hog nutrition Pork Industry Handbook, Michigan State University

 

Pork export and import

US Foreign Agricultural Trade Database

 

Compliance costs

US Environmental Protection Agency (EPA)

 

Pig slaughter capacity

National Pork Producer Council (NPPC)

 

 

Shipping costs

 

US Census Bureau, 1997 Economic Census

 

 

I‘ .5 '7

 

‘“ ”it; I J
l

Appendix 3

Appendix 3. 1 America’s top 25 hog producing counties, 1997

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 Rank 1992 Rank County State Inventory, 97

l 1 Duplin NC 2.034.349
2 2 Sampson NC 1,775,702
3 797 Ffexas OK 907,046
4 3 Sioux IA 762,294
5 28 Bladen NC 758,701
6 736 Sullivan MO Not Available
7 36 Wayne NC 529,439
8 16 'Martin MN 489,024
9 5 Plymouth IA 460,965
10 32 Hamilton IA 448,312
1 l 8 Washington IA 436.353
12 4 Delaware IA 401,729
13 1 14 'Mercer MO Not Available
14 34 Hardin IA 395,359
15 l l Greene NC 391,672
16 9 Carroll IA 372,598
17 182 Wright IA 358,616
18 25 Sac IA 350,473
1 9 6 Lancaster PA 349.774
20 77 Robeson NC 327.559
21 43 Blueearth MN 325.829
22 22 Lyon IA 325.619
23 23 Kossuth IA 323,029
24 100 Lenoir NC 315,588
25 133 [Pitt NC 303,393

 

 

153

Appendix 4

Appendix 4.1 U.S. per capita meat consumption and prices of meats 1970-99

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Prices of Meats“ Per Capita Consumption

Year Beef Chicken Pork Fish CPI FOOd Beef Pork Chicken Fish
1970 0.79 0.35 1.06 0.35 0.39 84.4 55.4 40.1 11.7
1971 0.82 0.36 0.96 0.39 0.40 83.7 60.2 40.1 1 1.5
1972 0.90 0.36 1.11 0.42 0.42 85.1 54.3 41.5 12.5
1973 1.08 0.52 1.48 0.48 0.48 80.4 48.5 39.7 12.7
1974 1.11 0.49 1.47 0.56 0.55 85.5 52.4 39.6 12.1
1975 1.12 0.55 1.80 0.60 0.60 88 42.7 38.8 12.1
1976 1.09 0.52 1.82 0.67 0.62 94.1 45.1 41.9 12.9
1977 1.09 0.52 1.72 0.75 0.66 91.5 46.7 42.7 12.6
1978 1.33 0.57 1.95 0.82 0.72 87.1 46.5 44.8 13.4
1979 1.69 0.59 1.98 0.89 0.80 77.9 53.2 47.6 13

1980 1.78 0.63 1.91 0.98 0.87 76.4 56.8 47.3 12.4
1981 1.80 0.65 2.09 1.06 0.94 77.2 54.2 48.7 12.6
1982 1.82 0.64 2.36 1.10 0.97 76.9 48.6 48.9 12.4
1983 1.80 0.65 2.34 1.11 0.99 78.5 51.3 49.1 13.3
1984 1.82 0.73 2.31 1.15 1.03 78.3 51 50.8 14.1
1985 1.78 0.70 2.31 1.20 1.06 79 51.5 52.4 15

1986 1.79 0.77 2.50 1.31 1.09 78.7 48.6 53.5 15.4
1987 1.93 0.76 2.71 1.45 1.14 73.7 48.8 56.6 16.1
1988 2.03 0.84 2.62 1.53 1.18 72.5 52.1 56.7 15.1
1989 2.16 0.92 2.64 1.60 1.25 68.9 51.5 58.3 15.6
1990 2.34 0.91 3.03 1.64 1.32 67.6 49.4 60.7 15

1991 2.40 0.88 3.13 1.66 1.36 66.6 49.9 63.1 14.8
1992 2.40 0.89 2.98 1.69 1.38 66.3 52.6 66.8 14.7
1993 2.49 0.93 3.07 1.75 1.41 64.9 52 69.5 14.9
1994 2.47 0.94 3.12 1.83 1.44 66.9 52.7 70.4 15.1
1995 2.45 0.95 3.15 1.92 1.48 67.3 52.2 69.8 14.9
1996 2.44 1.02 3.46 1.93 1.53 68 48.9 70.8 14.7
1997 2.48 1.06 3.64 1.98 1.57 66.7 48.5 71.8 14.5
1998 2.55 1.04 3.27 2.12 1.61 67.9 52.3 72.4 14.8
1999 2.58 1.06 3.32 2.07 1.64 68.9 53.7 77.3 15.2

 

 

 

 

 

 

 

 

 

 

*Nominal prices

154

 

Appendix 4.2 Meat Expenditure Shares 1970-1994

 

Meat Expenditure Share

 

Divisia volume

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Year Beef Pork Chicken Fish index

1970 0.4641 0.4091 0.0983 0.0285 .
1971 0.4747 0.3969 0.0979 0.0305 0.0291
I972 0.4881 0.3838 0.0947 0.0334 -0.0263
I973 0.4692 0.3868 0.11 10 0.0330 -0.0748
1974 0.4800 0.3890 0.0971 0.0339 0.0573
1975 0.4840 0.3764 0.1039 0.0357 -0.0665
1976 0.4760 0.3828 0.1007 0.0404 0.0632
1977 0.4700 0.3808 0.1048 0.0444 0.0010
1978 0.4759 0.3730 0.1061 0.0450 -0.0171
1979 0.4762 0.3807 0.1009 0.0421 0.0025
1980 0.4750 0.3783 0.1045 0.0422 0.0130
I981 0.4676 0.3812 0.1063 0.0449 -0.0091
1982 0.4683 0.3824 0.1039 0.0454 -0.0437
1983 0.4589 0.3898 0.1033 0.0480 0.0341
1984 0.4545 0.3753 0.1 I87 0.0515 0.0033
1985 0.4475 0.3788 0.1 164 0.0573 0.0147
1986 0.4349 0.3750 0.1279 0.0623 -0.0194
1987 0.4171 0.3878 0.1264 0.0686 -0.0163
1988 0.4 I 52 0.3853 0.I342 0.0653 0.0144
1989 0.4097 0.3739 0.1475 0.0688 -0.0193
I990 0.4079 0.3866 0.1420 0.0635 -0.0204
1991 0.4034 0.3939 0. I408 0.0619 0.0025
I 992 0.3976 0.3922 0.1479 0.0623 0.0267
1993 0.3920 0.3882 0.1564 0.0633 -0.0060
1994 0.3896 0.3888 0.1564 0.0652 0.0199
1995 0.3882 0.3872 0.1572 0.0674 -0.0036
1996 0.3805 0.3879 0.1664 0.0652 -0.0199
1997 0.3702 0.3947 0.1709 0.0642 -0.0090
I998 0.3841 0.3793 0.1670 0.0696 0.0387
I999 0.3787 0.3798 0.1745 0.0670 0.0286

 

Note: Calculated values are based on the data in Appendix 4.1

155

 

Appendix 4.3 Regression analysis of per capita pork demand
*Demand system estimation (Rotterdam Model)

Simultaneous equations estimation using reg3 command

Homogeneity restriction (imposed by eqn 1,2,3)

Symmetry restriction (imposed by eqn 4,5,6)

Adding up restriction (when we recover fish eqn, this restriction in maintained)
constraint define 1
[SBdlnPQ]dlnbeefp+[SBdlnPQ]dlnchkp+[SBdlnPQ]dlnpkp+[SBdlnPQ]dlnfshp=0
constraint define 2
[SBdlnBQ]dlnbeefp+[SBdlnBQ]dlnchkp+[SBdlnBQ]dlnpkp+[SBdlnBQ]dlnfshp=0
constraint define 3
[SBdlnCQ]dlnbeefp+[SBdlnCQ]dlnchkp+[SBdlnCQ]dlnpkp+[SBdlnCQ]dlnfshp=0
constraint define 4 [SBdlnPQ]dlnbeefp =[SBdlnBQ]dlnpkp

constraint define 5 [SBdlnPQ]dlnchkp = [SBdlnCQ]dlnpkp

constraint define 6 [SBdlnBQ]dlnchkp = [SBdlnCQ]dlnbeefp

Three stage regression method

reg3 (SBdlnPQ dlnbeefp dlnchkp dlnpkp dlnfshp DQ)(SBdlnBQ dlnbeefpdlnchkp dlnpkp
dlnfshp DQ)(SBdlnCQ dlnbeefp dlnchkp dlnpkp dlnfshp DQ),constraint (1-6)

 

 

Equation Obs Parms RMSE "R-sq" Chi2 P
SBdlnPQ 29 5 .0067 0.938 457.566 0.000
SBdlnBQ 29 5 .0073 0.809 139.703 0.000

 

 

 

SBdlnCQ 29 5 .0027 0.354 14.5327 0.005
Coef. Std. Err. z P>|z|
SBdlnPQ
dlnbeefp .1730 .0173 9.981 0.000
dlnchkp .0116 .0077 1.512 0.131
dlnpkp -.1906 .0194 -9.804 0.000
dlnfshp .0059 .0108 0.547 0.584
DO .4536 .0514 8.809 0.000
consl -.0007 .0012 -0.550 0.582
SBdlnBQ
dlnbeefp -.l938 .0209 -9.247 0.000
dlnchkp .0170 .0083 2.036 0.042
dlnpkp .1730 .0173 9.981 0.000
dlnfshp .0036 .0107 0.342 0.733
DQ .5004 .0510 9.812 0.000
cons -.0028 .0013 -2.137 0.033

 

156

 

SBdlnCQ

dlnbeefp .0171 .0083 2.036 0.042
dlnchkp -.0179 .0074 -2.403 0.016
dlnpkp .0117 .0077 1.512 0.131
dlnfshp -.0108 .0073 -1.474 0.140
DQ .0453 .0217 2.084 0.037
cons .0031 .0005 5.987 0.000

 

Endogenous variables: SBdlnPQ SBdlnBQ SBdlnCQ
Exogenous variables: dlnbeefp dlnchkp dlnpkp dlnfshp DQ

 

Three stage regression method (AIDS modfl

reg3 (dporksh dlnbeefp dlnchkp dlnpkp dlnfshp DQ)(dbeefsh dlnbeefp dlnchkp dlnpkp
dlnfshp DQ)(dchicsh dlnbeefp dlnchkp dlnpkp dlnfshp DQ),constraint (1-6)
Constraints:

( 1) [dporksh]dlnbeefp + [dporksh]dlnchkp + [dporksh]dlnpkp + [dporksh]dlnfshp = 0.0
( 2) [dbeefsh]dlnbeefp + [dbeefsh]dlnchkp + [dbeefsh]dlnpkp + [dbeefsh]dlnfsh p = 0.0
( 3) [dchicsh]dlnbeefp + [dchicsh]dlnchkp + [dchicsh]dlnpkp + [dchicsh]dlnfsh p = 0.0
( 4) [dporksh]dlnbeefp - [dbeefsh]dlnpkp = 0.0

( 5) [dporksh]dlnchkp - [dchicsh]dlnpkp = 0.0

( 6) [dbeefsh]dlnchkp - [dchicsh]dlnbeefp = 0.0

 

 

 

 

Equation Obs Parms RMSE "R-sq" Chi2 P
dporksh 29 5 .0067 0.25 20.08 0.00
dbeefsh 29 5 .0073 0.16 19.70 0.00
dchicsh 29 5 .0028 0.82 142.34 0.00
Coef. Std. Err. z P>|z]
dporksh
dlnbeefp -.0010 .0173 -0.059 0.953
dlnchkp -.0331 .0080 -4.105 0.000
dlnpkp .0499 .0194 2.568 0.010
dlnfshp -.0157 .0106 -1.481 0.139
DO .075 82 .0510 1.484 0.138
cons -.0007 .0012 -0.554 0.579

 

157

 

dbeefsh

 

dlnbeefp .0549 .0212 2.580 0.010
dlnchkp -.0347 .0087 -3.962 0.000
dlnpkp -.0010 .0173 -0.059 0.953
dlnfshp -.0191 .0107 -l .789 0.074
DQ .0447 .0510 0.876 0.381
cons -.0026 .0013 -1.989 0.047
dchicsh
dlnbeefp -.0347 .0087 -3.962 0.000
dlnchkp .08139 .00786 10.350 0.000
dlnpkp -.0331 .0080 -4.105 0.000
dlnfshp -.0135 .0074 -1.809 0.070
DQ -.0656 .0225 -2.910 0.004
cons l .0030 .0005 5.595 0.000

 

Endogenous variables: dporksh dbeefsh dchicsh
Exogenous variables: dlnbeefp dlnchkp dlnpkp dlnfshp DQ

 

Demand system estimation (Log-linear model)

Three-stage least squares regression

Constraints:

( 1) [lnporkq]dlnbeefp + [lnporkq]dlnchkp + [lnporkq]dlnpkp + [lnporkq]dlnfshp = 0.0
( 2) [lnbeefq]dlnbeefp + [lnbeefq]dlnchkp + [lnbeefq]dlnpkp + [lnbeefq]dlnfshp= 0. 0
( 3) [lnchicq]dlnbeefp + [lnchicq]dlnchkp + [lnchicq]dlnpkp + [lnchicq]dlnfshp= 0. 0

( 4) [lnporkq]dlnbeefp- [Inbeefq]dlnpkp= 0. 0

( 5) [lnporkq]dlnchkp- [lnchicq]dlnpkp= 0. 0

( 6) [lnbeefq]dlnchkp - [lnchicq]dlnbeefp = 0.0

 

 

 

 

Equation Obs Parms RMSE "R-sq" Chi2 P

lnporkq 29 5 .0573 0.285 10.382 0.03

lnbeefq 29 5 .1024 0.089 4.210 0.37
lnchicq 29 5 .2040 0.099 3.546 0.47

Coef. Std. Err. z P>1z|

lnporkq

dlnbeefp .1 196 .1506 0.795 0.427
dlnchkp .1694 .1794 0.944 0.345
dlnpkp -.3 836 .2104 -1.822 0.068
dlnfshp .0943 .2429 0.388 0.698
DQ .3956 .6126 0.646 0.518
cons 3.9256 .0130 300.42 0.000

 

158

 

 

 

Coef. Std. Err. z P>|z|

lnbeefq

dlnbeefp -.0537 .2297 -0.234 0.815
dlnchkp -.3754 .2858 -l.313 0.189
dlnpkp .l 1969 . 1506 0.795 0.427
dlnfshp .30948 .2537 1.220 0.223
DQ -.3751 .7672 -0.489 0.625
cons 4.3178 .0195 220.893 0.000
lnchicq

dlnbeefp —.3754 .285 -l .313 0.189
dlnchkp .7973 .5612 1.421 0.155
dlnpkp .1694 .1794 0.944 0.345
dlnfshp -.5912 .3997 - l .479 0.139
DQ 2.434 1.4335 1.698 0.090
cons 3.996 .0375 106.581 0.000

 

Endogenous variables: lnporkq lnbeefq lnchicq
Exogenous variables: dlnbeefp dlnchkp dlnpkp dlnfshp DQ

 

159

Appendix 4.4 Approximated pork demand (pounds) by states, 1997

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Demand/cap dj. emand/cap
State Region“ (Estimated) Factor (Adjusted) Populationl ‘97 Demand '97
Alabama S 47.6 1.12 53.312 4,320,281 230,322,821
Alaska W 47.6 0.83 39.508 608,846 24,054,288
Arizona W 47.6 0.83 39.508 4,552,207 179,848,594
Arkansas S 47.6 1.12 53.312 2,524.007 134,559,861
California W 47.6 0.83 39.508 32,217,708 1,272,857,208
Colorado W 47.6 0.83 39.508 3,891,293 153,737,204
Connecticut NE 47.6 0.8 38.08 3,268,514 124,465,013
DC S 47.6 1.12 53.312 735,024 39,185,599
Delaware S 47.6 1.12 53 .3 12 528,752 28,188,827
Florida S 47.6 1.12 53.312 14,683,350 782,798,755
Gggia S 47.6 1.12 53 .3 12 7,486,094 399,098,643
Hawaii W 47.6 0.83 39.508 1,189,322 46,987,734
Idaho W 47.6 0.83 39.508 1,210,638 47,829,886
Illinois ECB 47.6 1.15 54.74 12.01 1,509 657,510,003
Indiana ECB 47 .6 1.15 54.74 5,872,370 321,453,534
Iowa WCB 47 .6 1.15 54.74 2,854,396 156,249,637
Kansas S 47.6 1.12 53.312 2,616,339 139,482,265
Kentucky S 47.6 1.12 53.312 3,907,816 208,333,487
Louisiana S 47.6 1.12 53.312 4,351,390 231,981,304
Maine NE 47.6 0.8 38.08 1,245,215 47,417,787
Maryland S 47.6 1.12 53.312 5,092,914 271,513,431
lMassachusetts NE 47.6 0.8 38.08 6.1 15,476 232,877,326
Michigan ECB 47.6 1.15 54.74 9,785,450 535,655,533
Minnesota ECB 47.6 1.15 54.74 4,687,726 256,606,121
[Mississippi S 47.6 1.12 53.312 2,731,826 145,639,108
Missouri S 47.6 1.12 53.312 5,407.1 13 288,264,008
Montana W 47.6 0.83 39.508 878,706 34,715,917
N. Hampshire NE 47.6 0.8 38.08 1,656,042 63,062,079
Nebraska WCB 47.6 1.15 54.74 1,675,581 91,721,304
Nevada W 47.6 0.83 39.508 1,173,239 46,352,326
New Jersey NE 47.6 0.8 38.08 8,054,178 306,703,098
New Mexico W 47.6 0.83 39.508 1,722,939 68,069,874
New York NE 47.6 0.8 38.08 18,143,184 690,892,447
North Carolina S 47.6 1.12 53.312 7,428,672 396,037,362
North Dakota WCB 47.6 1.15 54.74 640,945 35,085,329
Ohio ECB 47.6 1.15 54.74 1 1,212,498 613,772,141
Oklahoma S 47 .6 1.12 53.312 3,314,259 176,689,776
Oregon W 47.6 0.83 39.508 3,243,254 128,134,479
Pennsylvania NE 47.6 0.8 38.08 12,015,888 457,565,015

 

 

 

 

 

 

 

 

160

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Rhode Island NE 47.6 0.8 38.08 986,966 37.583.665
South Carolina 8 47.6 1.12 53.312 3,790,066 202,055,999
South Dakota WCB 47.6 1.15 54.74 730,855 40,007,003
1Tennessee s 47.6 1.12 53.312 5,378,433 286,735,020
Texas 8 47.6 1.12 53.312 19,355,427 1,031,876,524
Utah W 47.6 0.83 39.508 2,065,397 81,599,705
Vermont NE 47.6 0.8 38.08 588,665 22,416,363
mnia s 47.6 1.12 53.312 6,732,878 358,943,192
Washington W 47.6 0.83 39.508 5,604,105 221,406,980
West Virginia 5 47.6 1.12 53.312 1,815,588 96,792,627
Wisconsin ECB 47.6 1.15 54.74 5,200,235 284,660,864
Wyoming_ W 47.6 0.83 39.508 480,031 18,965,065
Total (U.S.) 47.6 1 47.6 267,783,607 12,746,499,693

 

 

*S=South, W=West, NE=North East, ECB=Eastem Corn Belt, WCB=Western Corn Belt

161

4‘

 

“(NJ-I 3."?!
A!

Appendix 4.5 U.S. agricultural exports: Live animals and meats, 1997

State
labama

daho
llinois
ndiana

0W3

ontana

share %
0.47
0.90
0.08
3.18
5.09
0.78
0.86
1.44
4.40
1.25
9.96
12.86
3.98
0.02
1.95
4.18
0.48
1.33
0.31
15.12

Pork

1,519
911
266
10 12
I6 32

797
4680
14 84
4067
3 23
41741
1 912

61
6 25
I3 59
l 71
4 04
1.001
49052

State

ew
eW Mexico
ew York

. Carolina

. Dakota

lvania

. Carolina
. Dakota

CHI'ICSSCC

8X85

ash'

isconsin

otal

share °/o
0.01
0.04
0.29
0.32
2.43
0.40
0.66
0.37
0.08
2.86
0.14
2.31
0.47
13.10
1.02
1.62
1.91
3.09
0.25
100.00

Pork

 

45
134

933

1029

7891

1

128 1%
1 192
248
9 5 r
449 '
7 1
1 26
4 15
3.297
5
6188
10026
806
324,507

31"."

 

Source: Compiled from http://www.ers.usda.gov/data/FATUS/DATA/16010.xls

I62

Appendix 5

Appendix 5.1 Hog and pigs production and marketing

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Production Marketing Value of prod. Average Price Value Total pigs

(1,000 lbs) (1,000 lbs) ($1,000) ($/CWT) ($/head) IDec. l, 1997
AL 83,458 87,705 40,606 49 80 190,000
AK 87] 564 498 57 150 2,100
AZ 87,296 91,500 41,156 47 88 145,000
AR 254,014 260,945 143,175 56 79 860,000
CA 82,156 84,365 44,508 54 1 10 210,000
CO 347,895 345,910 198,448 57 88 790,000
CT 1,851 1,786 870 47 110 4,500
DE 13,719 13,525 6,331 46 79 30,000
FL 24,839 26,641 11,410 46 85 55,000
GA 230,861 254,877 1 13,699 49 81 520,000
Hl 6,340 6,105 5,091 80 130 29,000
ID 17,292 17,557 8,622 50 82 30,000
IL 1,819,944 1,860,100 928,630 51 83 4,700,000
IN 1,533,336 1,545,464 775,270 51 84 3,950,000
IA 5,419,830 5,439,021 2,801,426 52 85 14,600,000
KS 735,468 757,466 393,043 53 73 1,530,000
KY 255,202 263,026 13 5,731 53 74 570,000
LA 13,967 14.835 6.536 47 88 32,000
ME 3,572 2,542 1,679 47 88 6,000
MD 31,450 30,850 14,880 47 81 85,000
MA 4,250 3,403 1,998 47 88 18,500
MI 396,899 401,325 207,562 52 89 1,030,000
MN 2,080,925 2,083,120 1,] 12,009 53 85 5,700,000
MS 102,882 105 .660 51,599 50 86 220,000
MO 1,41 1,364 1,474,928 712,923 51 69 3,550,000
MT 62,465 61,145 33,310 53 85 180,000
NE 1,424,897 1,446,955 784,814 55 90 3,500,000
NV 4,454 4,608 2,375 53 1 10 7,500
NH 991 700 466 47 97 4,402
NJ 2,715 3,016 898 33 97 23,000
NM 2,213 2,288 955 43 88 6.000
NY 30,086 30,415 13,505 45 81 79,000
NC 3,827,575 3,793,557 2,071,550 54 72 9,600,000
ND 73,515 75,311 33,971 46 85 200,000
OH 769,772 762,900 403,338 52 79 1,700,000
OK 746,751 758,761 374,487 50 88 1,650,000
OR 16,440 16,320 9,354 57 88 35,000
PA 363,231 357,181 183,513 51 85 1,100,000
RI 1,204 1,162 566 47 85 2,800
SC 124,390 124,700 62,387 50 75 305,000
SD 544,203 538,633 293,001 54 84 1,400,000

 

 

I63

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

TN 143,047 155,287 72,219 50 75 340.000
TX 224,131 213.480 106.047 47 83 580,000
UT 84,510 65,040 49,676 59 88 295,000
VT 1.469 1,272 690 47 l 10 2,900
VA 141,783 136,730 73,020 52 75 400,000
WA 14,454 12,894 7,365 51 97 39,000
WV 7,402 6,855 3,533 48 85 16,000
WI 354,113 365,210 188,631 53 84 740,000
WY 5 3 ,728 58,128 24,474 46 97 95,000
U.S. 23,979,220 24,165,768 12,551,845 52 82 60,799,171

 

 

 

Source: http://usda.mannlib.comell.edu/usda/reports/general/sb/b9590599.txt

Appendix 5.2 Production of barley, sorghum and corn grain in selected states, 1997

 

 

-w—r1v

 

 

 

 

 

 

 

 

 

 

 

 

State Barley Corn State Sorghum Corn

(,000 Bu) (,000 Bu) (,000 Bu) (, 000 Bu)
N. Dakota 101,250 58,410 Kansas 273,000 371,800
Montana 63,600 1,890 Texas 185,850 241,500
Idaho 60,040 6,665 Nebraska 61,500 1,135,200
Washington 37,240 18,050 Missouri 40,920 299,000
Minnesota 27,540 85 1,400 Oklahoma 24,500 23,460
Colorado 10,080 143,080 Illinois 14,105 1,425,450
California 9,900 45,050 8. Dakota 1 1,360 326,400
Wyoming 9,200 7,020 Arkansas 1 1,100 23 ,125
Oregon 8,280 5,265 N. Mexico 10,340 14,875
Utah 8,170 2,940 Louisiana 7,546 48,789
USA 374,478 9,206,832 653,106 9,206,832

 

 

 

 

 

 

 

Source: Compiled from USDA/NASS database. http://www.nass.usda.gov: 81/ipedb/

164

Wnam rfl'

Appendix 5. 3 Comparison of wage rates and processing costs by selected states

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Adjusted cost
Hourly Adj. Average Processing Fixed cost Processing

State Wage ($) Factor Variable cost er head Per head Per head Region
Alabama 6.01 0.67 21 17.52 4.5 22.02 South
Arizona 9.47 1.05 21 21.57 4.5 26.07 West
Arkansas 7.53 0.84 21 19.30 4.5 23.80 South
California 9.11 1.01 21 21.15 4.5 25.65 West
Colorado 8.54 0.95 21 20.48 4.5 24.98 West
Connecticut 12.54 1.40 21 25.15 4.5 29.65 Northeast
Florida 6.59 0.73 21 18.20 4.5 22.70 South
Georgia 8.79 0.98 21 20.77 4.5 25.27 South
Idaho 8.77 0.98 21 20.75 4.5 25.25 West
Illinois 8.63 0.96 21 20.58 4.5 25.08 E,Com Belt
Indiana 9.34 1.04 21 21.41 4.5 25.91 E.Com Belt
Iowa 9.02 1.00 21 21.04 4.5 25.54 W.Com Belt
Kansas 9.09 1.01 21 21.12 4.5 25.62 W.Com Belt
Kentucky 8.84 0.98 21 20.83 4.5 25.33 South
Louisiana 6.79 0.76 21 18.43 4.5 22.93 South
Maine 8.83 0.98 21 20.82 4.5 25.32 Northeast
Maryland 8.26 0.92 21 20.15 4.5 24.65 South
Massachusetts 10.33 1.15 21 22.57 4.5 27.07 Northeast
Michigan 9.2 1.02 21 21.25 4.5 25.75 E. Corn Belt
Minnesota 9.56 1.06 21 21.67 4.5 26.17 E. Corn Belt
Mississippi 7.48 0.83 21 19.24 4.5 23.74 South

issouri 8.03 0.89 21 19.88 4.5 24.38 South
Montana 9.51 1.06 21 21.61 4.5 26.11 West
New Jersey 11.55 1.29 21 24.00 4.5 28.50 Northeast
New Mexico 8.73 0.97 21 20.70 4.5 25.20 West
New York 10.87 1.21 21 23.20 4.5 27.70 Northeast
North Carolina 8.16 0.91 21 20.04 4.5 24.54 South
North Dakota 8.52 0.95 21 20.46 4.5 24.96 W. Corn Belt
Ohio 11.24 1.25 21 23.63 4.5 28.13 E. Corn Belt
Oregon 9.84 1.10 21 22.00 4.5 26.50 West
Pennsylvania 9.92 1.10 21 22.09 4.5 26.59 Northeast
South Carolina 8.48 0.94 21 20.41 4.5 24.91 South
Tennessee 8.67 0.96 21 20.63 4.5 25.13 South
Texas 8.64 0.96 21 20.60 4.5 25.10 South
Virginia 9.29 1.03 21 21.36 4.5 25.86 South
Washington 9.68 1.08 21 21 .81 4.5 26.31 West
West Virginia 7.14 0.79 21 18.84 4.5 23.34 South
Wisconsin 10.45 1.16 21 22.71 [ 4.5 27.21 E.Com Belt
U.S. Average 8.99 1.00 21 21.00 4.5 25.50

 

Note: Compiled from Bureau of Labor Statistics (1998)

I65

 

Appendix 5.4 Average prices of inputs and market hogs in selected States, (1998)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mkt. hogs Corn price Soybean meal Wage Feeder pigs Region
State $/cwt $/bushel $/bushe1 S/hr $/cwt
Illinois 44.88 2.60 14.00 6.74 86.08 E. Corn Belt
Indiana 44.93 2.59 14.00 6.81 89.13 E. Corn Belt
Michigan 45.75 2.48 13.63 6.58 83-48 E. C0111 Belt
Ohio 46.40 2.57 14.00 6.39 78-98 E. COm BCIt
'Minnesota 47.63 2.36 13.63 7.03 91.17 E. Corn Belt
Wisconsin 44.13 2.48 13.63 5.92 33.13 E. Corn Belt
Maine 42.00 NA 15.53 NA 8898* North 5381
N, Jersey 39.93 2.82 15.53 6.86 3808* North East
Pennsylvania 44.03 2.96 15.53 5.93 8308* North East
N. York 40.55 2.88 15.53 6.37 88-03" North EaSt
Arkansas 44.00 2.57 15 .60 5.76 7325‘ SOUth
Florida 40.53 2.86 17.47 6.59 73.275 801101
Georgia 44.15 2.92 17.47 6.1 l 6308 South
Kentucky 45.65 2.68 14.03 5.68 72.43 501101
Louisiana 40.50 2.75 15.60 5.64 7325* 501101
[Maryland 42.15 2.88 15.53 6.27 7325* South
Missouri 44.75 2.61 14.00 5.92 74.48 South
Mississippi 45.88 2.66 15.60 5.39 7325* South
N. Carolina 47.08 2.87 16.20 5.85 7963 301101
Oklahoma 43.88 2.83 16.43 5.98 7.3-25* SOUth
S. Carolina 43.45 2.87 17.47 5.48 73.7-5* South
Tennessee 43.78 2.66 16.20 5.88 71 .67 South
Texas 40.98 2.78 16.43 5.56 73-25‘ 3011111
\LiEginia 46.50 2.76 16.20 6.02 7325* 50001
W. Virginia 40.03 2.90 16.20 5.62 7323"l 30001
Iowa 47.63 2.47 14.00 6.54 89-58 W. C0171 BCII
Kansas 44.78 2.60 16.20 6.84 83.23 W. C0111 3611
North Dakota 40.85 2.32 14.03 6.76 7325* W. C0111 Belt
Nebraska 48.10 2.52 14.03 6.39 90.80 W. Corn Belt
S. Dakota 47.20 2.30 14.03 5.66 88-02 W. Com BCII
Arizona 45.00 2.99‘ 20.17 6.00 83.33" West
California 48.28 3.23 20.17 6.57 83-38M WCSt
Colorado 48.48 2.66 20.17 6.08 3333" West
Idaho 43.88 3.22 21.30 6.32 8333'" WCSI
Montana 45.43 2.68 20.17 5.61 8338'” WCSI
N. Mexico 43.93 2.76 20.17 5.90 8338'” W651
Oregon 50.15 3.15 22.20 6.50 8333" West
Utah 44.90 3.25 20.17 5.99 8338’” West
Washington 45.48 2.99 22.20 7.08 3333" We“
W oming 44.58 2.79 20.17 5.32 33.38" West

 

 

 

 

 

 

 

 

"‘ Calculated on the basis of regional average " Based on national average

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Appendix 6
Appendix 6.1 Data and procedure for compliance cost estimation

The data for this analysis were obtained from an EPA publication entitled “Cost
Methodology Report for Swine and Poultry Sector”. The data include regulatory
compliance costs for different sized feeder to finish operations. Costs are categorized as
fixed costs, capital costs, and operation and management costs. Three-year recurring
costs and five-year recurring costs for manure management facilities maintenance are
suggested. In addition to cost, information on locations (Mid-Atlantic and Midwest
region), size of operation (Medium and Large), technology options, number of facilities
are also given in this data.

This research intended to find the compliance cost under the various technology
options in various geographic regions and sizes of operations. The U.S. EPA data were
used as a base for this analysis. Few assumptions were made before analyzing these costs.
The effective life of the manure management facility was expected to be ten years.
Manure management technology keeps changing and hog operations in ten years’ time
are likely to change significantly in size and even in existence. Capital costs and fixed
costs were incurred in the first year and operating costs every year in equal amounts.
Three-year recurring costs were incurred in year-1, year-3, year-6 and year-9. Similarly,
five-year recurring costs were incurred in year-1 and year-5. All these costs were put
together and discounted (10 %) to calculate the present values of costs. Annualized costs
per operation in different locations, technologies and sizes were derived. The annualized

discounted costs were calculated by the formula:

l7l

“wary-16!— .7. I“

 

 

(1+ i)” —1
i(1+ i)"

P :

 

where,

P=Sequence of level of cost in present value

A=Costs in each period

n=Number of year

i= interest rate (discount rate)

After calculation of annualized compliance cost per operation, weighted average costs for
different options, regions, and sizes were calculated.

Annualized cost per operation*No. of operations

 

Average cost=
Total number of operations in the group

Cost per operation

 

Cost per hog=
Inventory*Tumover

Number of hogs per operation and number of turnovers are critical assumptions. The U.S.
EPA assumed number of turnovers per year per operation as 2.8. The U.S. EPA has
given the range of inventory in size groups. Based on the given range, 3,000 hogs for
medium size and 4,000 hogs for large size of operations were assumed to calculate the

compliance cost per hog.

172

wdmsm‘fghl—I-nw

Appendix 6.2 Regulatory compliance costs for swine (grow to finish, 1997)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ID Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
1 1 Mid-Atlantic 288 Large 643 738 181
2 1 Mid-Atlantic 154 Large 883 975 1 89
3 2 Mid-Atlantic 173 Large 677 2,124 202
4 2 Mid-Atlantic 92 Large 968 4,444 241
5 3 Mid-Atlantic 28 Large 24,796 738 2,252
6 3 Mid-Atlantic 41 Large 24,830 2,124 2,273
7 3 Mid-Atlantic 1 5 Large 55,3 73 975 3,724
8 3 Mid-Atlantic 22 Large 55,459 4,444 3,776
9 3 Mid—Atlantic 88 Large 643 73 8 181
10 3 Mid-Atlantic 1 3 1 Large 677 2,124 202
l 1 3 Mid-Atlantic 47 Large 883 975 189
12 3 Mid-Atlantic 70 Large 968 4,444 241
13 4 Mid-Atlantic 2 8 Large 24,796 1,130 7,3 85
14 4 MidoAtlantic 41 Large 24,830 2,5 l 6 7,406
15 4 ‘ Mid-Atlantic 15 Large 55,373 1,367 8,857
16 4 Mid-Atlantic 22 Large 55,459 4,836 8,909
17 4 Mid-Atlantic 88 Large 643 1,130 5,314
18 4 Mid-Atlantic 131 Large 677 2,516 5,335
19 4 Mid-Atlantic 47 Large 883 1,367 5,322
20 4 Mid-Atlantic 70 Large 968 4,836 5,374
21 5 Mid-Atlantic 1 15 Large 1 19,903 73 8 2,566
22 5 Mid-Atlantic 173 Large 1 19,937 2,124 2,587
23 5 Mid-Atlantic 62 Large 290,930 975 5,990
24 5 Mid-Atlantic 92 Large 291,015 4,444 6,042
25 5 Mid-Atlantic 1 15 Large 757,409 738 24,775
26 5 Mid-Atlantic 173 Large 757,443 2,124 24,796
27 5 Mid-Atlantic 62 Large 1,894,502 975 61,729
28 5 Mid-Atlantic 92 Large 1,894,588 4,444 61,781
29 6 Mid-Atlantic 173 Large 98,039 27,124 -17,555
30 6 Mid-Atlantic 92 Lagge 173,966 29,444 -42,722
3 1 7 Mid-Atlantic 1 1 5 Large 643 73 8 18 1
32 7 Mid-Atlantic 173 Large 677 2,124 202
33 Mid-Atlantic 92 Large 968 4,444 241
34 7 Mid-Atlantic 62 Large 883 975 189
36 1 Mid-Atlantic 247 Medium 1.281 685 401
37 1 Mid-Atlantic 44 Medium 1,449 746 440
3 8 1 Mid-Atlantic 122 Medium 1,626 818 487
39 2 Mid-Atlantic 148 Medium 2,204 1,607 968
40 2 Mid-Atlantic 26 Medium 2,907 2,202 1,336
41 2 Mid-Atlantic 73 Medium 3 ,719 2,909 1,772
42 3 Mid-Atlantic 24 Medium 9,95 1 685 1 ,724
43 3 Mid-Atlantic 3 5 Medium 10,874 1,607 2,291
44 3 Mid-Atlantic 4 Medium 13,570 746 1,931
45 3 Mid-Atlantic 6 Medium 15,028 2,202 2,826

 

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1D Option Rgion # Facilities Size of oper. Capital cost Fixed cost Oper. cost
46 3 Mid-Atlantic 12 Medium 17,737 818 2,170
47 3 Mid-Atlantic 18 Medium 19,830 2,909 3.455
48 3 Mid-Atlantic 75 Medium 1,281 685 401
49 3 Mid-Atlantic 1 13 Medium 2,204 1,607 968
50 3 Mid-Atlantic 13 Medium 1.449 746 440

5 l 3 Mid-Atlantic 20 Medium 2,907 2,202 1,336
52 3 Mid-Atlantic 37 Medium 1,626 818 487
53 3 Mid-Atlantic 56 Medium 3,719 2,909 1,772
54 4 Mid-Atlantic 24 Medium 9,951 1,077 7,620
55 4 Mid-Atlantic 35 Medium 10,874 1,999 8,187
56 4 Mid-Atlantic 4 Medium 13,570 1,138 7,826
57 4 Mid-Atlantic 6 Medium 15,028 2,594 8,722
58 4 Mid-Atlantic 12 Medium 17,737 1,210 8,066
59 4 Mid-Atlantic 18 Medium 19,830 3,301 9,351
60 4 Mid-Atlantic 75 Medium 1,281 1,077 6,296
61 4 Mid-Atlantic 1 13 Medium 2,204 1,999 6,863
62 4 Mid-Atlantic 13 Medium 1,449 1 ,138 6,336
63 4 Mid-Atlantic 20 Medium 2,907 2,594 7,231
64 4 Mid-Atlantic 37 Medium 1,626 1,210 6,382
65 4 Mid-Atlantic 56 Medium 3,719 3,301 7,668
66 5 Mid-Atlantic 99 Medium 37,659 685 1.128
67 5 Mid-Atlantic 148 Medium 38,581 1,607 1,695
68 5 Mid-Atlantic 18 Medium 55,676 746 1,525
69 5 Mid-Atlantic 26 Medium 57,134 2,202 2,420
70 5 Mid-Atlantic 49 Medium 77,062 818 1,996
71 5 Mid-Atlantic 73 Medium 79,155 2,909 3,281
72 5 Mid-Atlantic 99 Medium 206,336 685 7,065
73 5 Mid-Atlantic 148 Medium 207,259 1,607 7,632
74 5 Mid-Atlantic 18 Medium 325,321 746 10,966
75 5 Mid-Atlantic 26 Medium 326,779 2.202 1 1,861
76 5 Mid-Atlantic 49 Medium 466,674 818 15,600
77 5 Mid-Atlantic 73 Medium 468,767 2,909 16,885
78 7 M id-Atlantic 99 Medium 1,281 685 401
79 7 Mid-Atlantic 148 Medium 2,204 1,607 968
80 7 Mid-Atlantic 18 Medium 1,449 746 440
81 7 Mid-Atlantic 26 Medium 2,907 2,202 1,336
82 7 Mid-Atlantic 49 Medium 1,626 818 487
83 7 Mid-Atlantic 73 Medium 3,719 2,909 1,772
84 1 Mid-Atlantic 89 Large 1 1,666 648 398
85 1 Mid-Atlantic 180 Large 19,006 760 545
86 2 M id-Atlantic 53 Large 86,744 1,041 16,038
87 2 Mid-Atlantic 108 Lage 208,015 1,219 39,504
88 3 Mid-Atlantic 9 Large 29,165 648 2,140
89 3 Mid-Atlantic 13 Large 104.243 1,041 17,780
90 3 Mid-Atlantic 17 Large 57,498 760 3,288
91 3 Mid-Atlantic 26 Large 246,507 1,219 42,247

 

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ID Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
92 3 Mid-Atlantic 27 Large 1 1,666 648 398
93 3 Mid-Atlantic 41 Large 86,744 1,041 16,038
94 3 Mid-Atlantic 55 Large 19,006 760 545
95 3 Mid-Atlantic 82 Large 208,015 1,219 39,504
96 4 Mid-Atlantic 9 Large 29, 165 1,040 7 ,273
97 4 Mid-Atlantic 13 Large 104,243 1,43 3 22,912
98 4 Mid-Atlantic 17 Large 57,498 1,152 8,421
99 4 Mid-Atlantic 26 Large 246,507 1,61 1 47,380
100 4 Mid-Atlantic 27 Large 1 1,666 1,040 5,53 1
101 4 Mid-Atlantic 41 Large 86,744 1 ,433 21,170
102 4 Mid-Atlantic 55 Large 19,006 1,152 5,678
103 4 Mid-Atlantic 82 Large 208,015 1,61 1 44,637
1 04 5 Mid-Atlantic 36 Large 86,735 648 37,096
105 5 Mid-Atlantic 53 Large 86,744 1,041 15,818
106 5 Mid-Atlantic 72 Large 208,003 760 37,346
107 5 Mid-Atlantic 108 Large 208,015 1,219 39,143
108 5 Mid-Atlantic 36 Large 658,057 648 26,184
109 5 Mid-Atlantic 53 Large 658,067 1,041 33,898
1 10 5 Mid-Atlantic 72 Large 1,645,966 760 64,952
1 1 1 5 Mid-Atlantic 108 Large 1,645,978 1,219 88,643
1 12 6 Mid-Atlantic 53 Large 184,106 26,041 -12,464
1 13 6 Mid-Atlantic 108 Large 381,013 26,219 -29,927
1 14 7 Mid-Atlantic 36 Large 15,218 648 1 1 ,3 87
1 15 7 Mid-Atlantic 53 Large 90,296 1,041 27,027
1 1 6 7 Mid-Atlantic 72 Large 24,844 760 18,609
1 17 7 Mid-Atlantic 108 Large 213,853 1,219 57,568
1 18 1 Mid-Atlantic 30 Medium 7,735 639 502

1 l9 1 Mid-Atlantic 5 Medium 41,31 1 639 1,327
120 1 Mid-Atlantic 24 Medium 10,3 1 1 709 594
121 2 Mid-Atlantic 18 Medium 28,955 1,026 5,056
122 2 Mid-Atlantic 3 Medium 41,698 1,026 7,518
123 2 Mid-Atlantic 14 Medium 57,1 12 1,319 10,619
124 3 Mid-Atlantic 3 Medium 14.425 639 1.727
125 3 Mid-Atlantic 4 Medium 3 5,645 1,026 6,282
126 3 Mid-Atlantic 0 Medium 50,425 639 2,669
127 3 M id-Atlantic 1 Medium 50,812 1,026 8,859
128 3 Mid-Atlantic 2 Medium 22,212 709 2,069
129 3 Mid-Atlantic 3 Medium 69,013 1,319 12,094
130 3 Mid-Atlantic 9 Medium 7,735 639 502
13 1 3 Mid-Atlantic 14 Medium 28,955 1,026 5,056
132 3 Mid-Atlantic 2 Medium 41,31 1 639 1,327
133 3 Mid-Atlantic 2 Medium 41,698 1,026 7,518
134 3 Mid-Atlantic 7 Medium 10,31 1 709 594
135 3 Mid-Atlantic 1 1 Medium 57,1 12 1,319 10,619
136 4 Mid-Atlantic 3 Medium 14,425 1,03 1 7,623
137 4 Mid-Atlantic 4 Medium 35,645 1,418 12,178

 

 

 

 

 

 

 

 

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1D Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
13 8 4 Mid-Atlantic 0 Medium 50,425 1,03 1 8,564
139 4 Mid-Atlantic 1 Medium 50,812 1,418 14,755
140 4 Mid-Atlantic 2 Medium 22,212 1,101 7,965
141 4 Mid-Atlantic 3 Medium 69,013 1,7 1 1 17,990
142 4 Mid-Atlantic 9 Medium 7,7 35 1,03 1 6,397
143 4 Mid-Atlantic 14 Medium 28,955 1,418 10,952
144 4 Mid-Atlantic 2 Medium 41,31 1 1,03 1 7,223
145 4 Mid-Atlantic 2 Medium 41,698 1 ,418 13,413
146 4 Mid-Atlantic 7 Medium 10,31 1 1 ,101 6,490
147 4 Mid-Atlantic 1 1 Medium 57,1 12 1,71 1 16,515
148 5 Mid-Atlantic 12 Medium 28,569 639 10,456
149 5 Mid-Atlantic 1 8 Medium 28,955 1,026 4,926
150 5 Mid-Atlantic 2 Medium 41,31 1 639 1,174
151 5 Mid-Atlantic 3 Medium 41,698 1,026 7,365
152 5 Mid-Atlantic 10 Medium 56,501 709 23,148
153 5 Mid-Atlantic 14 Medium 57,1 12 1,3 19 10,443
154 5 Mid-Atlantic 12 Medium 179,313 639 7,404
155 5 Mid-Atlantic 18 Medium 179,700 1,026 9,337
156 5 Mid-Atlantic 2 Medium 282,632 639 1 1,625
157 5 Mid-Atlantic 3 Medium 283,019 1,026 15,225
1 58 5 Mid-Atlantic 10 Medium 405,442 709 16,287
159 5 Mid-Atlantic 14 Medium 406,052 1,3 19 21,401
160 7 Mid-Atlantic 12 Medium 10,958 639 7,014
161 7 Mid-Atlantic 18 Medium 32,178 1,026 1 1,569
162 7 Mid-Atlantic 2 Medium 45,091 639 8,964
163 7 Mid-Atlantic 3 Medium 45,478 1,026 15,155
164 7 Mid-Atlantic 10 Medium 14,676 709 9,415
165 7 Mid-Atlantic 14 Medium 61,477 1,3 19 19,440
166 l Mid-Atlantic 81 Large 1 19,757 580 29,432
167 1 Mid-Atlantic 94 Large 290,778 5 80 73 ,215
168 2 Mid-Atlantic 49 Large 1 19,757 580 6,146
169 2 Mid-Atlantic 56 Lag 290,778 580 14,948
170 3 Mid-Atlantic 8 Large 143,910 580 31,503
171 3 Mid-Atlantic 12 Large 143,910 580 8,217
172 3 Mid-Atlantic 9 Large 345,269 580 76,750
173 3 Mid-Atlantic 13 Large 345,269 580 18,483
174 3 Mid-Atlantic 25 Large 1 19,757 5 80 29,432
175 3 Mid-Atlantic 37 Large 1 19,757 580 6,146
176 3 Mid-Atlantic 29 Large 290,778 5 80 73,215
1 77 3 M id-Atlantic 43 Large 290,77 8 580 14,948
1 78 4 Mid-Atlantic 8 Large 143 ,910 972 36,636
179 4 M id-Atlantic 12 Large 143,910 972 13,350
1 80 4 Mid-Atlantic 9 Large 345,269 972 81,882
1 81 4 Mid-Atlantic 13 Large 345,269 972 23,615
182 4 Mid-Atlantic 25 Large 1 19,757 972 34,564
183 4 Mid-Atlantic 37 Lage 1 19,757 972 1 1,279

 

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1D Option Regjon # Facilities Size of oper. Capital cost Fixed cost Oper. cost
1 84 4 Mid-Atlantic 29 Large 290,778 972 78,348
185 4 Mid-Atlantic 43 Large 290,778 972 20,081
186 5 Mid-Atlantic 32 Large 1 19,757 580 29,432
187 5 Mid-Atlantic 49 Large 1 19,757 580 6,146
1 88 5 Mid-Atlantic 38 Large 290,778 580 73,215
1 89 5 Mid-Atlantic 56 Large 290,778 580 14,948
190 5 Mid-Atlantic 32 Large 657,987 5 80 24,772
191 5 Mid-Atlantic 49 Large 657 ,987 580 24,772
192 5 Mid-Atlantic 3 8 Large 1,645,936 580 61,722
193 5 Mid-Atlantic 56 Large 1,645,936 580 61,722
194 6 Mid-Atlantic 49 Large 217,119 25,580 -1 1,61 1
195 6 Mid-Atlantic 56 Large 463,776 25,580 -28,015
196 7 Mid-Atlantic 32 Large 119,757 580 29,432
197 7 M id-Atlantic 49 Large 1 19,757 580 6,146
198 7 Mid-Atlantic 38 Large 290,778 580 73,215
199 7 Mid-Atlantic 56 Large 290,778 580 14,948
200 1 Mid-Atlantic 5 1 Medium 37,029 580 8,304
201 1 Mid-Atlantic 9 Medium 54,985 580 12,882
202 1 Mid-Atlantic 29 Medium 76,299 580 1 8,320
203 2 Mid-Atlantic 3 1 Medium 37,029 580 1,995
204 2 Mid-Atlantic 5 Medium 54,985 580 2,916
205 2 Mid-Atlantic 17 Medium 76,299 580 4,01 l
206 3 Mid-Atlantic 5 Medium 45,698 580 9,628
207 3 Mid-Atlantic 7 Medium 45,698 580 3,3 l 8
208 3 Mid-Atlantic 1 Medium 67,106 580 14,372
209 3 Mid-Atlantic 1 Medium 67,106 580 4,407
2 10 3 Mid-Atlantic 3 Medium 92,409 580 20,003
21 1 3 M id-Atlantic 4 Medium 92,409 580 5,694
212 3 Mid-Atlantic 16 Medium 37,029 580 8,304
2 13 3 Mid-Atlantic 23 Medium 37,029 580 1,995
214 3 Mid-Atlantic 3 Medium 54,985 580 12,882
215 3 Mid-Atlantic 4 Medium 54,985 580 2,916
216 3 Mid-Atlantic 9 Medium 76,299 580 18,320
2 17 3 Mid-Atlantic 13 Medium 76,299 580 4,01 l
218 4 Mid-Atlantic 5 Medium 45,698 972 15,523
219 4 Mid-Atlantic 7 Medium 45,698 972 9,214
220 4 Mid-Atlantic 1 Medium 67,106 972 20,268
221 4 Mid-Atlantic 1 Medium 67,106 972 10,302
222 4 Mid-Atlantic 3 Medium 92,409 972 25,899
223 4 Mid-Atlantic 4 Medium 92,409 972 1 1,589
224 4 Mid-Atlantic 16 Medium 37 ,029 972 14,200
225 4 Mid-Atlantic 23 Medium 37,029 972 7,890
226 4 Mid-Atlantic 3 Medium 54,985 972 1 8,777
227 4 Mid-Atlantic 4 Medium 54,985 972 8,812
228 4 M id-Atlantic 9 Medium 76,299 972 24,216
229 4 Mid-Atlantic 13 Medium 76,299 972 9,906

 

 

 

 

 

 

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1D Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost

230 5 Mid-Atlantic 20 Medium 37,029 580 8,304
231 5 Mid-Atlantic 31 Medium 37 ,029 580 1,995
232 5 Mid-Atlantic 4 Medium 54,985 580 12,882
233 5 Mid-Atlantic 5 Medium 54,985 580 2,916
234 5 Mid-Atlantic 12 Medium 76,299 580 18,320
235 5 Mid-Atlantic 17 Medium 76,299 580 4,01 1
236 5 Mid-Atlantic 20 Medium 178,806 580 6,960
237 5 Mid-Atlantic 31 Medium 178,806 580 6,960
238 5 Mid-Atlantic 4 Medium 282,143 580 10,824
239 5 Mid-Atlantic 5 Medium 282,143 580 10,824
240 5 Mid-Atlantic 12 Medium 404,903 580 15,414
241 5 Mid-Atlantic 17 Medium 404,903 580 15,414
242 7 Mid-Atlantic 20 Medium 37,029 580 8,304
243 7 Mid-Atlantic 3 1 Medium 37,029 580 1,995
244 7 Mid-Atlantic 4 Medium 54,985 580 12,882
245 7 Mid-Atlantic 5 Medium 54,985 580 2,916
246 7 Mid-Atlantic 12 Medium 76,299 580 18,320
247 7 Mid-Atlantic 17 Medium 76,299 580 4,01 1
248 1 Mid-West 356 Large 634 740 180
249 1 Mid-West 78 Large 920 1,050 188
250 2 Mid-West 214 Large 655 2,238 193
251 2 Mid-West 47 Large 982 5,447 227
252 3 Mid-West 39 [£86 27,458 740 2,515
253 3 Mid-West 59 Large 27,479 2,238 2,528
254 3 Mid-West 9 Large 70,800 1,050 4,600
255 3 Mid-West 13 Large 70,862 5,447 4,63 8
256 3 Mid-West 103 Large 634 740 180
257 3 Mid-West 155 Large 655 2,23 8 193
258 3 Mid-West 23 Lage 920 1,050 188
259 3 Mid-West 34 Large 982 5,447 227
260 4 Mid-West 39 Large 27,458 1,132 7,023
261 4 Mid-West 59 Large 27,479 2,630 7,036
262 4 Mid-West 9 Large 70,800 1,442 9,108
263 4 Mid-West 13 Large 70,862 5,839 9,146
264 4 Mid-West 103 Large 634 1,132 4,688
265 4 Mid-West 155 Large 655 2,630 4,701
266 4 M id-West 23 Large 920 1,442 4,696
267 4 Mid-West 34 Large 982 5,839 4,734
268 5 Mid-West 142 Large 1 15,51 1 740 2,478
269 5 Mid-West 214 Large 1 15,532 2,238 2,491
270 5 Mid-West 31 Large 327,306 1,050 6,716
271 5 Mid-West 47 Large 327,368 5,447 6,754
272 5 Mid-West 142 Large 728,228 740 23,826
273 5 Mid-West 214 Large 728,249 2,238 23,839
274 5 Mid-West 31 Large 2,136,432 1,050 69,589
275 5 Mid-West 47 Large 2,136,494 5,447 69,627

 

 

 

 

 

 

 

 

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1D Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
276 6 Mid-West 214 Large 155,261 27,238 ~15,110
277 6 Mid-West 47 Large 362,771 30,447 -43,296
278 7 Mid-West 142 Large 634 740 180
279 7 Mid-West 214 Large 655 2,238 193
280 7 Mid-West 31 Large 920 1.050 188
281 7 Mid-West 47 Large 982 5,447 227
283 1 Mid-West 1432 Medium 1,222 651 571
284 l Mid-West 256 Medium 1,360 692 595
285 1 Mid-West 314 Medium 1,520 748 625
286 2 Mid-West 859 Medium 1,780 1,314 914
287 2 Mid-West 154 Medium 2,243 1,740 1,137
288 2 Mid-West 188 Medium 2,839 2,313 1,435
289 3 Mid-West 157 Medium 10,721 651 2,069
290 3 Mid-West 236 Medium 1 1,279 1,314 2,412
291 3 Mid-West 28 Medium 14,587 692 2,273
292 3 Mid-West 42 Medium 15,470 1,740 2,815
293 3 Mid-West 34 Medium 19,612 748 2,539
294 3 Mid-West 52 Medium 20,930 2,313 3,348
295 3 M id-West 416 Medium 1,222 651 571
296 3 Mid-West 623 Medium 1,780 1 ,314 914
297 3 Mid-West 74 Medium 1,360 692 595
298 3 Mid-West 1 l 1 Medium 2,243 1,740 1,137
299 3 M id-West 91 Medium 1,520 748 625
300 3 Mid-West 137 Medium 2,839 ‘ 2,313 1,435
301 4 Mid-West 157 Medium 10.721 1,043 5,408
302 4 Mid-West 236 Medium 1 1,279 1,706 5,751
303 4 Mid-West 28 Medium 14,587 1,084 5,611
304 4 Mid-West 42 Medium 15.470 2,132 6,153
305 4 Mid-West 34 Medium 19,612 1,140 5.877
306 4 Mid-West 52 Medium 20.930 2,705 6,687
307 4 Mid-West 416 Medium 1,222 1,043 3,910
308 4 Mid-West 623 Medium 1,780 1,706 4,253
309 4 Mid-West 74 Medium 1,360 1,084 3.933
310 4 Mid-West l l 1 Medium 2243 2,132 4.475
31 1 4 Mid-West 91 Medium 1.520 1 , 140 3,964
312 4 Mid-West 137 Medium 2,839 2,705 4,774
313 5 Mid-West 573 Medium 35,584 651 1,258
314 5 Mid-West 859 Medium 36,142 1,314 1,601
315 5 Mid-West 102 Medium 52,421 692 1,616
316 5 Mid-West 154 Medium 53,303 1,740 2,158
317 5 Mid-West 126 Medium 75,036 748 2.096
318 5 Mid-West 188 Medium 76,355 2,313 2,905
319 5 Mid-West 573 Medium 192,862 651 6,799
320 5 Mid-West 859 Medium 193,421 1,314 7,142
321 5 Mid-West 102 Medium 304.152 692 10,435
322 5 Mid-West 154 Medium 305,035 1,740 10,977

 

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1D fition Region # Facilities Size of (3361'. Capital cost Fixed cost Oper. cost
323 5 Mid-West 126 Medium 453,791 748 15,324
324 5 Mid-West 188 Medium 455,110 2,313 16,133
325 7 Mid-West 573 Medium 1,222 651 571
326 7 Mid-West 859 Medium 1,780 1,314 914
327 7 Mid-West 102 Medium 1.360 692 595
328 7 M id-West 154 Medium 2,243 1,740 1,137
329 7 Mid-West 126 Medium 1,520 748 625
330 7 Mid-West 188 Medium 2,839 2,313 1,435
331 1 Mid-West l 10 Large 1 1.452 699 394
332 1 Mid-West 92 Large 20,421 892 573
333 2 Mid-West 66 Large 83,631 1,379 13,896
334 2 Mid-West 55 Large 233,808 1,689 39,975
335 3 Mid-West 12 Large 30,914 699 2,364
336 3 Mid-West 18 Large 103,093 1,379 15,866
33 7 3 M id-West 10 Large 69,666 892 3 .963
338 3 Mid-West 15 Large 283,053 1,689 43,365
339 3 Mid-West 32 Large 1 1,452 699 394
340 3 M id-West 48 Large 83,631 1,379 13 ,896
341 3 Mid-West 27 Large 20.421 892 573
342 3 Mid-West 40 Large 233,808 1,689 39,975
343 4 M id-West 12 Large 30,914 1,091 6,872
344 4 Mid-West 18 Large 103,093 1,771 20,374
345 4 M id-West 10 Large 69,666 1,284 8,471
346 4 Mid-West 15 Large 283,053 2,081 47,873
347 4 Mid-West 32 Large 1 1.452 1,091 4,902
348 4 Mid-West 48 Large 83,631 1,771 18,404
349 4 Mid-West 27 Large 20,421 1,284 5,081
350 4 Mid-West 40 Large 233,808 2,081 44,483
3 51 5 M id-West 44 Large 83,621 699 6,641
3 52 5 Mid-West 66 Large 83,631 1,379 13,680
353 5 Mid-West 37 LaLge 233,797 892 24,071
354 5 Mid-West 55 Large 233,808 1,689 39,587
355 5 Mid-West 44 Large 632,705 699 24,558
356 5 Mid-West 66 Large 632,715 1,379 25,875
357 5 Mid-West 37 Large 1,856,159 892 72,043
358 5 Mid-West 55 Large 1,856,170 1,689 78,040
359 6 Mid-West 66 Large 23 8,237 26,379 -10,717
360 6 Mid-West 55 Large 595,597 26,689 -30.331
361 7 Mid-West 44 Large 14,937 699 1 1,178
362 7 Mid-West 66 Large 87,1 16 1,379 24,680
363 7 Mid-West 37 Large 26,702 892 20,006
364 7 Mid-West 55 Large 240,089 1,689 59,408
365 1 Mid-West 171 Medium 7,586 653 698
366 1 Mid-West 30 Medium 8,755 651 720
367 1 Mid-West 62 Medium 10,199 734 790
368 2 Mid-West 103 Medium 7,971 1.1 10 1,842

 

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1D Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
369 2 Mid-West 18 Medium 39,438 1,1 10 6,639
370 2 Mid-West 37 Medium 55,744 1,459 9.597
371 3 Mid-West 19 Medium 14,947 653 2,091
372 3 Mid-West 28 Medium 15,332 1,1 10 3,234
373 3 Mid-West 3 Medium 18,735 651 2,238
374 3 Mid-West 5 Medium 49,418 1,1 10 8,157
375 3 Mid-West 7 Medium 23,581 734 2,471
376 3 Mid-West 10 Medium 69,126 1,459 1 1,277
377 3 Mid-West 50 Medium 7,586 653 698
378 3 Mid-West 74 Medium 7,971 1,1 10 1,842
379 3 Mid-West 9 Medium 8,755 651 720
380 3 Mid-West 13 Medium 39,438 1,1 10 6,639
381 3 Mid-West 18 Medium 10,199 734 790
382 3 Mid-West 27 Medium 55,744 1,459 9,597
3 83 4 Mid-West 19 Medium 14,947 1,045 5,429
3 84 4 Mid-West 28 Medium 15,332 1,502 6,572
3 85 4 Mid-West 3 Medium 18,735 1,043 5,576
386 4 Mid-West 5 Medium 49,418 1,502 1 1,495
387 4 Mid-West 7 Medium 23,581 1,126 5,809
388 4 Mid-West 10 Medium 69,126 1,851 14,615
3 89 4 Mid-West 50 Medium 7,5 86 1,045 4,037
390 4 Mid-West 74 Medium 7,971 1,502 5,180
391 4 Mid-West 9 Medium 8,755 1,043 4,059
392 4 Mid-West 13 Medium 39,438 1,502 9,978
393 4 Mid-West 18 Medium 10,199 1,126 4,129
394 4 Mid-West 27 Medium 55.744 1,851 12,935
395 5 Mid-West 68 Medium 27,131 653 1,091
396 5 Mid-West 103 Medium 27,516 1,1 10 2,234
397 5 Mid-West 12 Medium 39,051 651 15,442
398 5 Mid-West 18 Medium 39,438 1,1 10 6,490
399 5 Mid-West 25 Medium 55,134 734 17,748
400 5 Mid-West 37 Medium 55,744 1,459 9,422
401 5 Mid-West 68 Medium 184,409 653 6,926
402 5 Mid-West 103 Medium 168,034 1,1 10 7,414
403 5 Mid-West 12 Medium 264,302 651 10,864
404 5 Mid-West 18 Medium 264,688 1,] 10 1 1,597
405 5 Mid-West 25 Medium 394,335 734 ' 15,580
406 5 Mid-West 37 Medium 394,945 1,459 16,944
407 7 Mid~West 68 Medium 10,740 653 7,072
408 7 Mid-West 103 Medium 1 1,125 1,1 10 8,215
409 7 Mid-West 12 Medium 12,441 651 8,169
410 7 Mid-West 18 Medium 43,124 1,110 14,088
41 1 7 Mid-West 25 Medium 14,513 734 9,508
412 7 Mid-West 37 Medium 60,058 1,459 18,314
413 1 Mid-West 101 Large 1 15,367 580 28,308
414 1 Mid-West 48 Large 327,157 580' 82,531

 

 

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ID Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
415 2 Mid-West 61 Large 1 15,367 580 5,920
416 2 Mid-West 29 Large 327,157 580 16,821
417 3 Mid-West l 1 Large 142,191 580 30,643
418 3 Mid-West 17 Large 142,191 580 8,255
419 3 Mid-West 5 Large 397,037 580 86,942
420 3 Mid-West 8 Large 397,037 580 21,232
421 3 Mid-West 29 Large 1 15,367 580 28,308
422 3 Mid-West 44 Large 1 15.367 580 5,920
423 3 Mid-West 14 Large 327,157 580 82,531
424 3 Mid-West 21 Large 327,157 580 16,821
425 4 Mid-West 1 1 Large 142,191 972 35,151
426 4 M id-West 17 Lara 142,191 972 12,763
427 4 Mid-West 5 Large 397,037 972 91,450
428 4 Mid-West 8 Large 397,037 972 25,740
429 4 Mid-West 29 Large 1 15,367 97 2 32,816
430 4 Mid-West 44 Large 1 15,367 972 10,428
431 4 Mid-West 14 Large 327,157 972 87,039
432 4 M id-West 21 Large 327,157 972 21,329
433 5 M id-West 40 Large 1 15,367 580 28,308
434 5 Mid-West 61 Large 1 15,367 580 5,920
435 5 Mid-West 19 Large 327,157 580 82,531
436 5 Mid-West 29 Large 327,157 580 16,821
437 5 Mid-West 40 Large— 632,635 580 23,824
438 5 Mid-West 61 Large 632,635 580 23 ,824
439 5 Mid-West 19 Large 1,856,136 580 69,585
440 5 Mid-West 29 Large 1 ,856, 136 580 69,5 85
441 6 Mid-West 61 Large 269,973 25,580 -9,383
442 6 Mid-West 29 Large 688,946 25,580 -26,702
443 7 Mid-West 40 Large 1 15,367 580 28,308
444 7 Mid-West 61 Large 1 15,367 580 5,920
445 7 Mid-West 19 Lara 327,157 580 82,531
446 7 Mid-West 29 Large 327,157 580 16,821
447 1 Mid-West 294 Medium 34,999 580 7,986
448 1 Mid-West 53 Medium 51,801 580 12,268
449 1 Mid-West 74 Medium 74,370 580 18.027
450 2 Mid-West 176 Medium 34,999 580 2,089
451 2 Mid-West 32 Medium 51,801 580 2,951
452 2 Mid-West 44 Medium 74,370 580 4,1 10
453 3 Mid-West 32 Medium 44,498 580 9,484
454 3 Mid-West 48 Medium 44,498 580 3.587
455 3 Mid-West 6 Medium 65,028 580 13,946
456 3 Mid-West 9 Medium 65,028 580 4,629
457 3 Mid-West 8 Medium 92,462 580 19,940
458 3 Mid-West 12 Medium 92,462 580 6,023
459 3 Mid-West 85 Medium 34,999 580 7,986
460 3 Mid-West 128 Medium 34,999 580 2.089

 

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ID Option Region # Facilities Size of oper. Capital cost Fixed cost Oper. cost
461 3 Mid-West 15 Medium 51,801 580 12,268
462 3 Mid-West 23 Medium 51,801 580 2,951
463 3 Mid-West 21 Medium 74,370 580 18,027
464 3 Mid-West 32 Medium 74,370 580 4,1 10
465 4 Mid-West 32 Medium 44,498 972 12,822
466 4 Mid-West 48 Medium 44,498 972 6,925
467 4 Mid-West 6 Medium 65,028 972 17,285
468 4 Mid-West 9 Medium 65,028 972 7,968
469 4 Mid-West 8 Medium 92,462 972 23,278
470 4 Mid-West 12 Medium 92,462 972 9,362
471 4 Mid-West 85 Medium 34,999 972 1 1,324
472 4 Mid-West 128 Medium 34,999 972 5,428
473 4 Mid-West 15 Medium 51,801 972 15,607
474 4 Mid-West 23 Medium 51,801 972 6,290
475 4 Mid-West 21 Medium 74,3 70 972 21,365
476 4 Mid-West 32 Medium 74,370 972 7,449
477 5 Mid-West 1 18 Medium 34,999 580 7,986
478 5 Mid-West 176 Medium 34,999 580 2,089
479 5 Mid-West 21 Medium 51,801 580 12,268
480 5 Mid-West 32 Medium 51,801 580 2,951
48 1 5 Mid-West 30 Medium 74,370 580 1 8,027
482 5 Mid-West 44 Medium 74,370 580 4,1 10
483 5 Mid-West 1 18 Medium 167,137 580 6,723
484 5 Mid-West 176 Medium 167,137 580 6,723
485 5 Mid-West 21 Medium 263,81 1 580 10,337
486 5 Mid-West 32 Medium 263,81 1 5 80 10,337
487 5 Mid-West 30 Medium 393,794 580 15,197
488 5 Mid-West 44 Medium 393 ,794 5 80 15,197
489 7 Mid-West 1 18 Medium 34,999 580 7,986
490 7 Mid-West 176 Medium 34,999 580 2,089
491 7 Mid-West 21 Medium 51,801 580 12,268
492 7 Mid-West 32 Medium 51,801 580 2,951
493 7 Mid-West 30 Medium 74,370 580 1 8,027
494 7 Mid-West 44 Medium 74,370 580 4,1 10

 

183

 

Appendix 6.2 Continued for additional variables

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual

[D 3 yr recurrent 5 Yr recurrent Cost/operation
1 255 0 3748.5306 374.85
2 51 1 0 54282369 542.82
3 1,750 0 12018.314 1201.83
4 4.252 0 26119.019 2611.9
5 255 2,703 48621.43 4862.14
6 1,750 2.703 56891.213 5689.12
7 51 1 2.703 90533.347 9053.33
3 4,252 2,703 111225.13 11122.51
9 255 2,703 10470.492 104705
10 1.750 2,703 18740.275 1374113
11 5” 2,703 12150.198 121502
12 4.252 2.703 32840.98 3284.1
13 255 2703 83707.499 8370.75
14 1.750 2703 91977.282 9197.73
15 511 2.703 125619.42 12561.94
16 4,252 2,703 1463112 14631.12
17 255 2.703 45556.561 4555.66
18 1.750 2.703 53826.344 5382.63
19 51 1 2.703 47236.267 4723.63
20 4,252 2,703 67927.05 6792.7
21 255 0 1391288 13912.88
22 1,750 0 147398.59 14739.3(,
23 51 1 0 334684.33 33468.43
24 4,252 0 355375.12 35537.51
25 255 O 926745.96 92674.6
26 1,750 0 935015.75 93501.57
2., 5“ 0 23149976 231499.76
28 4,252 0 23356893 233568.93
29 1.750 5,000 26794.588 2679.46
30 4.252 5,000 -53836.661 -5333157
31 255 0 3748.5306 374.85
32 1.750 0 12018.314 120133
33 4.252 0 26119.019 2611.9
34 51 1 0 54282369 542.82
36 227 0 5694.884 ~ 569.49
37 292 0 6479.1313 647.91
38 368 0 7386.8061 738.68
39 1,198 0 15728.984 1572.9
40 1,825 0 22327.561 2232,76
41 2,570 0 30136.2 3013.62

 

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1997 1997 Present value Average annual
1D 3 yr recurrent 5 yr recurrent Cost/cyneration
42 227 2.854 30404.548 3040.45
43 1.198 2.854 40438.648 4043.86
44 292 2.854 35775.31] 3577.53
45 1,825 2.854 51616.982 5161.7
46 368 2,854 41970.719 4197.07
47 2.570 2.854 64720.112 6472.01
48 227 2.854 12792.36 1279.24
49 1.193 2.854 22826.459 2282.65
50 292 2,854 13576.607 1357.66
51 1.825 2,854 29425.036 2942.5
52 3 68 2,854 14484.282 1448.43
53 2,570 2.854 37233.675 3723.37
54 227 2.854 70647.752 7064.78
55 1.198 2.854 80681.852 8068.19
56 292 2,854 76011.757 7601.18
57 1.825 2,854 91860.186 9186.02
58 368 2.854 82213.923 8221.39
59 2,570 2.854 104963.32 10496.33
60 227 2,854 53028.805 5302.88
61 1.198 2,854 63062.905 6306.29
62 29;, 2,854 53819.81] 5381.98
63 1.825 2,854 69661.482 6966.15
64 368 2,854 54720.727 5472.07
65 2570 2,854 77476.88 7747.69
66 227 0 46986.694 4698.67
67 1.198 0 57019.794 5701.98
68 292 0 68039.672 6803.97
69 1.825 0 83881.343 8388.13
70 3 68 0 93022.173 9302.22
71 2,570 0 115771.57 11577.16
72 227 0 255792.02 25579.2
73 1.198 0 265826.12 26582.61
74 292 0 401496.62 40149.66
75 1.825 0 417338.29 41733.83
76 368 0 574583.93 57458.39
77 2570 O 597333.33 59733.33
78 227 0 5694.884 569.49
79 1,198 0 15728.984 1572.9
80 292 O 6479,1313 647.91
81 1.325 0 22327.561 2232.76
82 368 0 7386.8061 738.68

 

 

 

 

 

185

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual
ID 3 yr recurrent 5 YT recurrent Cost/operation
83 2,570 0 30136.2 3013.62
34 159 207,255 531130.01 53113
85 279 493323 1263957 126395.?
86 582 198797.57 19879.76
87 775 O 479719.79 4797193
88 159 209.958 567125.19 56712.52
89 582 2.703 234792.75 23479.28
90 279 501.026 1327711 132771.]
91 775 2,703 543473.75 54347.38
92 159 209,958 537851.97 5 3 73 5,2
93 582 2.703 205519.53 2055195
94 279 501,026 1270679 1270679
95 775 2,703 486441.75 48644.17
96 159 209.958 602211.26 60221.13
97 582 2.703 269872.06 26987.21
98 279 501,026 1362797.] 136279.71
99 775 2.703 578559.82 57855.98
'00 159 209,958 572938.04 57293.8
101 582 2.703 240598.84 24059.88
102 279 501.026 l305765.l 13057651
103 775 2,703 521527.82 5215178
104 159 0 338829.16 33882.92
105 582 0 197310.59 1973106
106 279 O 462437.34 46243.73
‘07 775 0 477279.78 47727.98
108 159 0 836396.69 8363967
109 582 0 89083674 89083.67
1 10 279 0 20869899 208698.99
1 1 1 775 0 22498145 224981.45
1 13 532 5.000 140948.14 14094.31
1 I3 775 5.000 220866.26 22086.63
1 14 1 59 207,255 608956.92 60895.69
1 15 582 0 276624.48 27662.45
1 16 279 498.323 13918901 139189.01
1 17 775 0 607652.79 6076528
118 180 41,347 115398.53 11539.85
119 180 0 51726.858 5172.69
120 253 100,722 266650.74 2666507
121 586 66783.92 6678.39
122 586 96167.636 9616.76
123 896 134225.29 1 3422_ 53

 

 

 

 

 

 

186

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual
ID 3 yr recurrent 5 Yr recurrent Cost/operation
124 ,80 44.201 137465.81 1374353
125 586 2,854 88857.958 8885.8
126 130 2,854 77008944 770039
127 586 2.854 121442.96 12144.3
128 253 103,576 295618.78 2956133
129 896 2,854 163193.33 1631933
130 180 44,201 122496.01 12249.6
131 586 2,854 73881.395 733314
132 180 2.854 58824.334 5332,43
133 586 2,854 103265.11 10326.51
,34 253 103,576 273748.22 2737432
135 396 2,854 141322.77 1413223
136 180 44,201 177709.02 177703
137 586 2,854 129101.16 12910.12
138 180 2,854 117245.39 ] 172454
139 586 2.854 161686.17 16168.62
140 253 103,576 335861.98 33586.2
141 896 2,854 203436.53 2034335
142 180 44,201 162732.45 1527325
143 586 29854 1141246 11412.46
144 180 2,854 99067.538 9906.75
145 586 2,854 143501.56 14350.16
146 253 103,576 313991.42 3139914
147 896 2.854 181565.97 18156.6
148 180 0 100687.99 10068.8
149 586 0 65905.247 6590.52
150 180 0 50692.727 506927
15] 586 0 95133.506 9513.35
152 253 0 214803.06 2143031
153 896 0 133035.71 13303.57
1 54 130 0 230803.45 23030 34
155 586 0 2464643 24646.43
156 180 0 362652.29 36265.23
157 536 0 389580.43 3395304
158 253 0 517370.39 5173704
159 896 0 556041.09 55604.11
160 180 41.347 1626363 16263.63
161 586 0 114028.44 1 1402.84
162 ,80 0 107125.52 1071255
163 586 0 1515663 15156.63
164 253 100,722 330637.09 33063.71

 

 

 

 

 

187

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual
1D 3 yr recurrent 5 )1" recurrent Cost/operation
165 896 0 198211.64 19821.16
166 0 0 319268.59 31926.86
167 0 0 786219.93 78621.99
168 0 0 161877.96 16187.8
169 0 0 392391.89 39239.19
170 0 2.703 364141.49 36414.15
171 0 2,703 206750.86 20675.09
172 0 2,703 871326.04 87132.6
173 0 2,703 477498 477498
174 0 2,703 325990.55 3259905
175 0 2,703 168599.92 16859.99
176 0 2,703 792941.89 79294_ 19
177 0 2,703 399113.85 3991133
178 0 2.703 399227.56 3992276
179 0 2.703 241836.93 24183.69
1 80 O 2.703 906405.35 90640.53
181 0 2.703 512577.31 51257.73
132 0 2.703 361069.86 36106.99
183 0 2703 203685.99 20368.6
184 0 2.703 828027.96 82802.8
185 0 2.703 434199.92 4341999
186 0 0 319268.59 31926.86
187 0 0 161877.96 16187.8
188 0 0 786219.93 78621.99
189 0 0 392391.89 39239.19
190 0 0 826001.54 82600.15
191 0 0 826001.54 82600.15
192 0 0 20636965 206369.65
193 0 0 2063696. 5 206369.65
194 0 5.000 176654.23 17665.42
'95 0 5.000 312436.21 3124362
196 0 0 319268.59 31926.86
197 0 0 161877.96 16187.8
.98 0 0 786219.93 78621.99
199 0 0 392391.89 39239.19
200 0 0 93735.934 9373.59
20, 0 0 142634.74 14263.47
202 0 0 200704.32 2007043
203 0 0 51093.253 5109.33
204 0 0 75274.313 7527.43
205 0 0 103989.44 10393.94

 

 

 

 

 

188

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual

ID 3 yr recurrent 5 yr recurrent Cost/operation
206 0 2.854 118451.36 11845.14
207 0 2.854 75801.917 7580.19
208 0 2,854 171924.17 17192.42
209 0 2.854 104570.49 10457.05
2,0 0 2,854 235287.23 23528.72
2” 0 2.854 138572.36 13857.24
2,2 0 2,854 100833.41 10083.34
213 0 2.854 58190.728 5819.07
2 , 4 0 2.854 149732.22 14973.22
215 O 2.854 82371.789 8237.18
2, 6 0 2,854 207801.79 20780.18
2 1 7 0 2,854 11 1086.92 11108.69
2,8 0 2,854 1586878 15868.78
2 1 9 0 2.854 116045.12 11604.51
220 0 2,854 212167.37 21216.74
22, 0 2.854 144806.94 14480.69
222 0 2.854 275530.43 27553.04
223 0 2.854 1788088 17880.88
224 0 2.854 141076.61 14107.66
225 0 2,854 98427.173 9842.72
226 0 2.854 189968.67 18996.87
227 0 2.854 122614.99 12261.5
228 0 2,854 248045 24804.5
229 0 2,854 151323.37 15132.34
230 0 0 93735.934 9373.59
231 0 0 51093.253 5109.33
232 0 0 142634.74 14263.47
233 0 0 75274.313 7527.43
234 0 0 20070432 20070.43
:35 0 0 103989.44 10398.94
2 36 O 0 226428.81 22642.88
237 0 0 226428.81 22642.88
238 O 0 355882.67 35588.27
239 0 0 355882.67 35588.27
240 0 0 509666.59 50966.66
24, 0 0 509666.59 50966.66
242 0 0 93735.934 9373.59
243 0 0 51093.253 5109.33
244 0 0 142634.74 14263.47
245 0 0 75274.313 7527.43
246 0 0 200704.32 20070.43

 

 

 

 

 

189

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual
ID 3 yr recurrent 5 W recurrent Cost/operation
247 0 0 103989.44 10398.94
248 252 0 3721.311 372. 13
249 576 0 58251232 582.51
250 1,313 0 12354.589 1235.46
251 5,172 0 31169.297 3116.93
252 252 2.370 52221.471 5222.15
253 1.818 2.370 60854.748 6085.47
254 576 2,370 111419.78 1114193
255 59172 2,370 136757.19 1367572
256 252 2.370 9615.1502 961.52
257 1.818 2370 18248428 1824.84
258 576 2,370 11718.962 1171.9
259 5.172 2,370 37063.136 370631
260 252 2,370 83083.15 830832
261 1.818 2,370 91716.428 9171.64
262 576 2.370 142281.45 1422815
263 5,172 2,370 167618.87 16761.89
264 252 2.370 40476.83 4047.68
265 1.818 2.370 49110.107 4911_01
266 576 2,370 42580.642 425806
267 5.172 2,370 67918.056 6791,81
268 252 0 134130.55 1341305
269 1.818 0 142763.83 14276.38
270 576 0 376334.03 37633.4
271 5,172 0 401671.45 4016714
272 252 0 891139.19 89113.92
273 1,818 0 899772.47 89977.25
274 576 0 26104201 261042.01
2.75 5,172 ‘ 0 26357576 263575.75
276 1.818 5.000 100961.51 10096.15
277 5.172 5,000 136219.56 13621.96
278 252 0 3721.311 372.13
279 1,818 0 12354.589 1235.46
280 576 0 5825.1232 532.51
281 5,172 0 31169.297 3116.93
283 19] 0 6589.3913 65894
284 234 0 7123.5425 712.35
285 292 0 78025507 780.26
286 884 0 13238125 132381
287 1.329 0 17631.036 1763.1
288 1.923 0 23501.85 2350,18

 

 

 

 

 

190

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual

ID 3 yr recurrent 5 yr recurrent Cost/operation
289 191 1048 33793334 3379.33
290 884 3.048 40442.067 404421
291 234 3,048 39272.109 392721
292 1.329 3,048 49779603 497796
293 292 3.048 46411.247 4641.12
294 1,923 3.048 62102.787 6210.28
295 19] 3,048 14169.316 1416.93
296 884 3,048 20818.05 2081.8
297 234 3.048 14703.467 147035
298 1,329 3.048 25210.961 2521.1
299 292 1043 15382-476 1538.25
300 1.928 3,048 31081.775 310313
301 191 3.048 56753.714 5675.37
302 884 3.048 63402.448 6340.24
303 234 3.048 62225.731 6222.57
304 1,329 3.048 72733.225 7273.32
305 292 3.048 69364.869 6936.49
306 1,928 3.048 85063.168 8506.32
307 191 3.048 37129.697 3712.97
308 884 3.048 43778.43 4377.84
309 234 3.048 37657.089 3765.71
310 1.329 3.048 48164583 4316,46
31 1 292 3.048 38342.856 3834.29
312 1.928 3048 54042155 5404.22
313 191 0 45594.841 4559.48
314 884 0 52243.574 5224.36
315 234 0 65085.506 6508.55
316 1.329 0 75592 7559.2
317 292 0 91261.075 9126.11
313 1.928 0 106953.61 1069536
3,9 ,9, 0 240324.59 2403246
320 884 0 246974.33 24697.43
32] 234 0 376424.34 3764243
322 1,329 0 386931.83 3869318
323 292 0 559424.44 55942.44
324 1.928 0 575116.98 57511.7
325 191 0 65893913 658.94
326 884 0 13238.125 132381
327 234 0 7123.5425 712.35
323 1.329 0 17631.036 1763.1
329 292 O 7802.5507 780.26

 

 

 

 

 

191

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual

ID 3 yr recurrent 5 W recurrent Cost/operation
330 1,928 0 23501.85 2350.l8
331 209 85.939 229469.38 2294694
332 410 342,942 879871.53 87987.15
333 920 0 1830613 18306.13
334 1.244 0 511270.62 51127.06
335 209 88.309 2681405 2681405
336 920 2.370 221732.41 22173.24
337 4,0 345.312 957923.46 9579235
338 1.244 1370 589322.55 58932.26
339 209 88.309 235363.22 2353632
340 920 2,370 188955.14 13395.51
34, 4,0 345.312 885765.36 3357654
342 1.244 2.370 517164.46 51716.45
343 209 88.309 299002.18 2990022
344 920 2.370 252594.09 2525941
345 4,0 345.312 988785.13 9837351
346 1.244 2.370 620184.23 62018.42
347 209 88.309 2662249 26622.49
348 920 2370 219816.82 21981.68
349 410 345,312 916627.04 916627
350 1.244 2.370 548026.14 54802.61
351 209 0 130144.43 13014.44
352 920 0 181601.35 1816013
353 410 0 399225.07 3992251
354 1.244 0 508648.12 50864.81
3 5 5 309 0 800329.86 3003299
356 920 0 813111.65 81311.16
357 410 0 2345831 234583.]
358 1.244 0 23909149 239091.49
359 920 5.000 208741.71 2037417
360 1.244 5000 435293.95 43529.4
361 209 85.939 305843.69 30584.37
362 920 0 259435.61 2594356
363 410 342.942 10175006 101750.06
364 1.244 0 648899.73 64889.97
365 '94 0 13827248 1332,72
366 19] 50.244 140078.88 1400739
367 278 15.133 55153505 551535
368 671 0 24541.8 2454.18
369 671 0 88431.837 8843.18
370 1.035 0 126713.24 12671.32

 

 

 

 

 

192

 

 

1997

1997

Present value

Average annual

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ID 3 yr recurrent 5 W recurrent Cost/operation
37] 194 3,048 38183.493 3818.35
372 671 3,048 48891.286 488913
373 191 53.292 167899 16789.9
374 671 3.048 116251.96 [1625.2
375 278 18.181 87477.349 8747.73
376 1.035 3.048 159030.33 1590303
377 194 3,048 21407.173 2140.72
378 671 3.048 32121.724 3212.17
379 ‘9] 53,292 1476588 14765.88
380 671 3,048 96011.762 9601.18
381 278 18,181 62733.43 627334
332 1.035 3.048 134293.17 1342932
383 194 3.048 61137.114 6113.71
384 671 3,048 71844.90? 7184.49
385 191 53.292 190852.62 19085.26
336 671 3.048 139205.58 13920.56
387 278 18,181 110430.97 11043.1
388 1.035 3.048 181983.95 18198.39
389 194 3,048 44367.553 4436.76
390 671 3.048 55075.346 5507.53
391 191 53.292 170619.18 17061.92
392 671 3.048 118972.14 11897.21
393 278 18.181 85693.81 856938
394 1.035 3.048 157246.79 15724.68
395 194 0 36028.544 3602.85
396 671 0 46736.337 4673.63
397 19' 0 144931.83 1449313
393 671 0 87424.742 374247
399 278 0 1770745 17707.45
400 1.035 0 125530.41 1255304
401 194 0 232745.45 23274.54
402 671 0 222266.08 22226.61
403 19] O 339240.02 33924
404 671 0 347193.08 34719.3]
405 278 0 501621.94 5016219
406 1.035 0 515572.79 5155723
407 ‘94 0 60063.266 6006.33
408 67, 0 70771.058 7077.11
409 [9, 50.244 194112.85 1941123
410 671 0 142465.81 14246.58
411 278 15.133 118392.67 11839.27

 

 

 

 

 

193

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual
1D 3 Yr recurrent 5 yr recurrent Cost/operation
4,2 1.035 0 189945.65 18994.57
413 0 0 307281.45 3072814
4,4 0 0 885565.99 88556.6
4 1 5 0 0 155960.42 15596.04
4,6 0 0 441430.54 44143.05
4,7 0 2,370 355781.61 35578.16
4 , 8 0 2370 204460.58 20446.06
4,9 0 2,370 991153.89 99115.39
420 0 2,370 547018.43 54701.84
42, 0 2,370 313175.29 31317.53
422 0 2.370 161854.26 16185.43
423 0 2,370 891459.83 89145.98
424 0 2,370 447324.38 44732.44
425 0 2370 386643.29 38664.33
426 o 2370 235322.26 23532.23
427 0 2,370 10220156 102201.56
428 0 2,370 577880.11 57788.01
429 0 2.370 344036.96 34403.7
430 0 2,370 192715.94 19271.59
43, 0 2,370 922321.51 92232.15
432 o 2370 478186.06 47818.61
433 0 0 307281.45 3072814
434 o 0 155960.42 15596.04
435 0 0 885565.99 88556.6
436 0 0 441430.54 44143.05
4 37 0 0 794241.98 79424.2
438 0 0 794241.98 79424.2
439 0 0 23270427 232704.27
440 0 0 23270427 232704.27
44, 0 5.000 244567.34 24456.73
442 0 5.000 546480.81 54648.08
443 0 0 307281.45 30728.14
444 0 0 155960.42 15596.04
445 0 O 885565.99 88556.6
446 0 0 441430.54 44143.05
447 0 0 89556.564 8955.66
448 0 0 1353007 13530.07
449 0 0 196794.92 19679.49
45,, 0 0 49698.601 4969.86
45, o 0 72326879 7232.69
452 0 0 102729.59 10272.96

 

 

 

 

 

194

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1997 1997 Present value Average annual
1D 3 yr recurrent 5 yr recurrent Cost/operation
453 0 3.048 1 16760.51 1 1676.05
454 0 3,048 76902543 7690.25
455 0 3.048 167449.27 16744.93
456 0 3.048 104475.45 10447.54
457 0 3.048 235396.86 23539.69
458 0 3,048 141331.53 14133.15
459 0 3,048 97136.489 9713.65
460 0 3.048 57278.526 5727.85
46, 0 3.048 142880.63 14288.06
462 0 3,048 79906804 7990.68
463 0 3.048 204374.85 2043743
464 0 3.048 110309.51 1 1030.95
465 0 3.048 139714.13 13971.41
466 0 3.048 99856.165 9985.62
467 0 3,048 190409.65 1904097
468 0 3.048 127435.83 12743.58
469 0 3.048 258350.48 25835.05
470 0 3.048 164291.91 16429.19
47, 0 3.048 1200901 1 12009.01
472 0 3.048 80238.906 8023.89
473 0 3.048 165841.01 16584.1
474 0 3,048 102867.18 10286.72
475 0 3,048 227328.47 22732.85
476 0 3.048 133269.89 13326.99
477 0 0 89556564 8955.66
478 0 0 49698.60] 4969.86
479 0 0 1353007 13530.07
480 0 0 72326.879 7232.69
43, 0 0 196794.92 19679.49
482 0 0 102729.59 10272.96
483 0 0 213157.92 21315.79
484 0 0 213157.92 21315.79
485 0 0 334259.03 33425.9
486 0 0 334259.03 33425.9
487 0 0 497090.88 49709.09
433 0 497090.88 4970909
489 0 0 89556.564 8955.66
49,, 0 0 49698.601 4969.86
49, 0 0 1353007 13530.07
492 0 0 72326.879 7232.69
493 0 O 196794.92 19679.49

 

 

 

 

 

 

195

 

1997 1997 Present value
1D 3 yr recurrent 5 yr recurrent

Average annual
Cost/operation

 

 

 

 

 

494 0 0 102729.59

 

10272.96

 

*Source: United States Environmental protection Agency, 2001

(httQzl/www.ena.gov/ost/gu ide/ca fo/pd f/PPCostReport.pd1')

Definitions of the variables listed in the above table
ID= Identification Number

Option=Technology option adopted in these operations
Region=Geographical location

# of facilities= Number of facilities in the category
Capital= Capital investments for waste management
Operart= Operation and management cost

3 year recurrent=Cost reoccurring in every 3 years

5 year recurrent= Cost reoccurring in every 5 years

Compliance cost data analvsis

 

There are some outliers in the data. Remove outlier by hadimvo method in STATA

programming.

hadimvo costphog,gen (odd)

Beginning number of observations: 489
Initially accepted: 2
Expand to (n+k+1)/2: 245
Expand, p = .05: 458
Outliers remaining: 31

The results say that observation 459 to 491 are outliers (odd) and therefore are dropped.
We are interested in compliance costs per hog by size of operation, production region and

the underlying technology options

Size 1=medium-sized operation
Size 2=large-sized operation
Region l= Mid-Atlantic
Region 2= Mid-West

Technology options: technology 1 to technology 7 (Described in Chapter 6, section 6.4))

196

 

Appendix 6.3 Environmental compliance cost per pig by location, size of operation
and technology options (descriptive statistics)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Region Size Option Cost (Mean) Range

1 l 1 0.31 0002-058
1 1 2 0.37 0.36-1.23
l l 3 1.36 0.10-5.39
1 l 4 1.25 0.08-6.38
l l 5 1.08 0.01-5.89
1 1 6 2.12 0.72-6.47
1 1 7 0.74 0.18-2.62
2 1 1 0.46 0.01-1.34
2 1 2 0.16 0.01-0.37
2 1 3 1.15 0.03-4.04
2 1 4 1.46 1.36-6.24
2 1 5 1.16 0.01-5.00
2 1 6 1.18 0.16-2.63
2 1 7 0.52 0.10-1.66
1 2 1 0.47 005-131
1 2 2 1.45 0.03-4.41
1 2 3 2.41 0.02-5.85
1 2 4 2.43 0.49-6.90
1 2 5 1.90 (077)-7.04
1 2 6 - -

l 2 7 1.59 0.10-4.09
2 2 1 0.16 0003-088
2 2 2 0.42 .0023-1 .65
2 2 3 0.77 0009-407
2 2 4 0.85 0.02-2.85
2 2 5 1.16 0009-586
2 2 6 - -

2 2 7 0.63 0014-3.17

 

 

 

 

 

Size 1=medium-sized operation, Size 2=large-sized operation
Region 1= Mid-Atlantic, Region 2= Mid-West
Technology options: technology 1 to technology 7

197

 

Appendix 6.4 Environmental compliance costs by states and regions

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

“.3-

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Region (this
State EPA Region study) Compliance costs per hog
Small egium ar e

AL South South 0.31 0.81 1.05
AR South South 0.31 0.81 1.05
AZ Central West 0.31 0.81 1.05
CA Pacific West 0.31 0.81 1.05
CO Central West 0.3 1 0.81 1 .05
CT lMid-Atlantic Northeast 0.39 1.95 1.13
FL South South 0.31 0.81 1.05
GA South South 0.31 0.81 1.05
IA Midwest W. Corn Belt 0.31 0.81 1.05
ID Central West 0.31 0.81 1.05
[IL idwest E. Corn Belt 0.31 0.81 1.05
KS Midwest W. Corn Belt 0.31 0.81 1.05
KY id-Atlantic South 0.39 1.95 1.13
LA South South 0.31 0.81 1.05

A id-Atlantic Northeast 0.39 1 .95 1 .13
'MD id-Atlantic South 0.39 1.95 1.13
MB Mid-Atlantic Northeast 0.39 1 .95 1.13
ILVII jMidwest E. Corn Belt 0.31 0.81 1.05
[1er [Midwest E. Corn Belt 0.31 0.81 1.05
lMO lMidwest South 0.31 0.81 1.05
NS South South 0.31 0.81 1.05
MT Central West 0.31 0.81 1.05
NC [Mid-Atlantic South 0.39 1.95 1.13
ND Midwest w Corn Belt 0.31 0.81 1.05
NE Midwest W. Corn Belt 0.31 0.81 1.05
N] Mid-Atlantic Northeast 0.39 1.95 1.13

J Mid-Atlantic Northeast 0.39 1.95 1.13
NM Central West 0.31 0.81 1.05
NV Central West 0.31 0.81 1.05
NY Mid-Atlantic Northeast 0.39 1 .95 1 .13
OH Midwest E. Corn Belt 0.31 0.81 1.05
OK Central South 0.31 0.81 1.05
OR Pacific West 0.31 0.81 1.05
PA Mid-Atlantic Northeast 0.39 1.95 1.13
SC South South 0.31 0.81 1.05
SD [Midwest W. Corn Belt 0.31 0.81 1.05
TN Mid-Atlantic South 0.39 1.95 1.13
TX Central South 0.31 0.81 1.05
UT Central W. Corn Belt 0.31 0.81 1.05

 

 

 

 

 

 

198

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Region (this
State EPA Region study) Compliance costs per hog
Small Medium argg

VA Mid-Atlantic South 0.39 1.95 1.13
WA Pacific West 0.31 0.81 1.05
WI Midwest E. Corn Belt 0.31 0.81 1.05
WV lMid-Atlantic South 0.39 1.95 1.13
WY Central est 0.31 0.81 1.05
Appendix 7

Appendix 7.1 Shipping cost as a function of volume and distance

verage mile

Truck Transportation Dollar (million) Tons (1000 Tons) er shipment S/cwt/mile
Less than 50 lb 189,451 9,546 11 1 8.13
50 to 99 1b 102,809 9,264 127 3.97
100 to 499 lb 499,753 70,727 173 1.86
500 to 7491b 182,787 36,230 204 1.12
750 to 999 1b 135.940 30,553 206 0.98
1000 to 9.999 lb 1,368,634 544,479 205 0.56
10.000 to 49,999 lb 2,121,594 3,957,795 167 0.15
50,000 to 99,999 lb 296,824 2,162,393 74 0.08
100,000 lb or more 83,741 879,688 86 0.05
Less than 50 miles 1,729,620 5,212,913 25 0.60
50 to 99 miles 500,926 866,735 50 0.53
100 to 249 miles 835.764 770,562 125 0.39
250 to 499 miles 709,017 415,852 250 0.31
500 to 749 miles or more 431,281 191,915 375 0.27
750 to 999 miles 259.706 103.369 500 0.23
1000 to 1499 miles 239,934 79,277 750 0.18
1500 to 1999 miles 149,645 37.500 1,000 0.18
2000 miles or more 125,637 22,552 1,400 0.18
Average Shipment cost:

Live animals 6,173 5,922 272 0.17
Meat 153,843 71,952 136 0.71

 

 

 

 

 

 

Source: Transportation commodity flow survey, U.S. Census Bureau, 1997 Economic

Census.

199

 

 

 

Appendix 7.2 Minimizing total cost of production, processing and transportation

OPTION LlMCOL = O, LlMROW = 0;
SET PDREGION Pooled production regions

/ AL,AR,AZ,CA,CO,FL,GA,1A,1D,IL,IN,KS,KY,LA.MD,MI,MN.MS,MO,MT,NE,NV,NM,NY,

NC,ND,OH,OK,ORG,PA,SC,SD,TN,TX,UT,VA,WA,W1,WY,NH /;
SET TYPE Production types within regions

/ SMALL, MEDIUM, LARGE /;
SET PCREGION Processing regions

/ AR,CA,1A,1D.1L,IN,KS,KY,MN.MO.MS,NC,ND,NE.OH,OK,ORg,PA,SC,SD,TN, TX,VA,WI /;
SET MARKET Markets

/ AL,AR,AZ,CA,CO,CT,DC,DE,FL,GA,1A,ID,1L,IN,KS,KY,LA,MA,MD,ME,M1,

MN,MO,MS,MT,NC,ND,NE,N1-1,NJ,NM.NV,NY,OH,OK,ORg,PA,R1,SC,SD,TN,

TX.UT,VA,VT,WA,W1,WV,WY, EX/ ;

SET MKT (MARKET)

/AL,AR,AZ,CA,CO,CT,DC,DE,FL.GA,IA,lD,1L,IN,KS,KY,LA,MA,MD,ME,MI,

MN ,MO,MS,MT,NC,ND,NE,NH,NJ,NM,NV,NY,OH,OK,ORg,PA,R1,SC,SD,TN,

TX,UT,VA,VT,WA,W1.WV,WY/;

MKT (MARKET)=YES;

MKT ('EX')=NO;
TABLE DIST] (PDREGION,PCREGION) 1N MILES FROM PRODUCTION TO PROCESSING
REGION
TABLE DIST2(PCREGION,MARKET) 1N MILES FROM PROCESSING TO MARKET
TABLE PRODCOST(PDREGION,TYPE) Production cost $1000 per 1000 head

SMALL MEDIUM LARGE
AL 128.77 105.22 96.48
AR 126.43 104.38 93.78
AZ 158.06 128.66 114.92
CA 160.49 130.92 117.14
CO 154.99 125.76 112.01
FL 131.87 109.46 98.82
GA 132.39 111.04 99.37
[A 138.94 113.14 100.20
ID 161.83 132.19 118.43
1L 134.74 113.77 101.01
1N 134.68 1 13.70 100.94
KS 143.18 117.10 104.15
KY 125.34 103.36 92.77

LA
MD
MI
MN
MO
MS
MT
NC
ND
NE
NH
NM
NV
NY
OH
OK
Org
PA
SC
SD
TN
TX
UT
VA
WA
WI
WY

AL
AZ
AR
CA
CO
FL

GA

128.17 107.09 95.44
129.37 107.13 96.51
133.04 1 12.20 99.46
132.14 111.26 98.48
124.78 103.88 92.22
127.18 105.10 94.53
155.03 125.83 112.11
130.07 107.80 97.21
137.63 111.88 98.93
139.41 113.60 100.67
147.66 127.65 118.26
158.01 128.89 115.42
155.87 126.60 112.86
148.02 129.12 118.72
134.29 113.41 100.68
130.04 107.77 97.16
176.79 146.03 132.39
148.52 129.64 119.29
131.76 110.47 98.84
137.18 111.56 98.65
128.11 105.95 95.35
129.54 108.38 96.74
160.49 130.96 117.22
129.16 108.00 96.34
161.11 131.47 117.66
132.72 112.02 99.33
155.97 126.73 113.03 ;
TABLE PRODCAP(PDREGION,TYPE) Production capacity (1000 head)
SMALL MEDIUM LARGE
74.952 74.952 190.787
78.195 78.195 199.039
111.5 466.275 435.866
72.097 72.097 183.521
295.611 295.611 752.465
22.767 22.767 57.953
227.716 326.723 435.631
15.004 15.004 38.192

ID

201

 

IL
IN
IA
KS
KY
LA
MD
MI
MN
MS
MO
MT
NE
NV
NM
NY
NC
ND
OH
OK
Org
PA
SC

NH

2384.435
1861.041
6444.004
735.594
337.17
12.678
405
420.916
2427.565
90.296
1260.459
52.254
2304.486
3.938
1.956
25.993
294.721
64.36
151 1.378
176.845
13.947
374.617
106.567
847.39
132.707
182.438
55.582
122.706
11.019
794.449
49.676
9.285

3034.735
2521.409
9190.628
676.747
388.256
12.678
40.5
670.348
3236.753
90.296
1375.046
52.254
1854.831
3.938
1.956
25.993
3831.379
64.36
1096.49
353.689
13.947
638.236
106.567
533.542
132.707
182.438
55.582
122.706
11.019
539.09
49.676
9.285

24.39
1620.906
5493.249
1530.036
296.301
32.271
103.091
467.684
2427.565
229.844
3093.854
133.01
1461.381
10.024
4.977
66.163
10609.974
163.826
355.618
2416.874
35.501
374.617
271.263
711.389
337.799
464.387
141.483
312.344
28.049
846.519
126.447
23.635;

202

 

 

PARAMETER PROCCOST(PCREGION) Processing cost $1000 per 1000 HOGS

‘value of by-products ($7.625 per hog)is subtracted from the processing cost

/ AR 18.445
CA 18.025
1A 17.915
ID 17.625
1L 17.455
IN 18.285
KS 17.995
KY 17.705
MN 18.545
MO 16.755
MS 16.115
NC 16.915
ND 17.335
NE 17.875
OH 20.505
OK 17.635
Org 18.875
PA 18.965
sc 17.285
SD 17.875
TN 17.505
TX 17.475
VA 18.235
w1 19.585 / ;

PARAMETER PROCCAP(PCREGION) Processing capacity (1000 head)
/ AR 351

CA 1872
IA 30667
ID 169
11. 8502
IN 7280
KS 416
KY 2145
MN 8242
M0 4368

203

MS 1690

NC 8320
ND 239.2
NE 7150
OH 962
OK 2080
OR 143
PA 2028
SC 780
SD 3900
TN 520
TX 208
VA 4758
WI 650 / ;

PARAMETER DEMAND(MARKET) Market demand (million lbs)
/AL 230.32

AZ 179.85
AR 134.56
CA 1272.86
CO 153.74
CT 124.47
DE 28.19
DC 39.19
FL 782.80
GA 399.10
1D 47.83
[L 657.51
1N 321.45
1A 156.25
KS 139.48
KY 208.33
LA 231.98
ME 47.42
MD 271.51
MA 232.88
Ml 535.66

204

 

MN
MS
MO
MT
N E
NV
NH
NJ
NM
NY
NC
ND
OH
OK
ORG
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
EX

256.61
145.64
288.26
34.72
91.72
46.35
63.06
306.70
68.07
690.89
396.04
35.09
613.77
176.69
128.13
457.57
37.58
202.06
40.01
286.74
1031.88
81.60
22.42
358.94
221.41
96.79
284.66
18.97
784.35 / ;

SCALAR TRATEI TRANSPORTATION RATE PER 1000 LIVE HOGS PER MILE IN $1000

/0.125/;

SCALAR TRATE2 TRANSPORTAION RATE PER 1000 CWT PORK IN $1000

/0.05/ ;

SCALAR CFl Conversion factor live wt to processed pork (61 percent)

/ 0.61 /;

SCALAR CF2 Conversion factor CWT to head (1 hog= 250 pounds)

/ 2.5 /;

205

SCALAR CF3 conversion factor million pounds to 1000 cwt
/ 0.1 / ;
VARIABLES
LIVEPROD (PDREGION, TYPE) Total production of hogs by type and region (1000 head)
LIVESHIP (PDREGION, PCREGION) shipment of live hogs (1000 head)
TOTALPROC (PCREGION) Hog slaughtered (1000 hogs)
PORKSHIP (PCREGION, MARKET) shipment of pork (1000 cwt processed pork)
TOTCOST total cost in 1000 of dollars;
POSITIVE VARIABLES
LIVEPROD,
LIVESHIP,
TOTALPROC,
PORKSHIP ;
PARAMETER TCl (PDREGION, PCREGION) Cost of transporting 1000 hogs from production region to
processing region (1000's of 3);
TC] (PDREGION, PCREGION)=(TRATE1*DIST1 (PDREGION, PCREGION»;
PARAMETER TC2 (PCREGION,MARKET) Cost of transporting 1000 CWT pork from processing region
to market (1000 of 5);
TC2 (PCREGION, MARKET) = (TRATE2*DIST2 (PCREGION. MARKET»;
EQUATIONS
PRODUCTCAP (PDREGION, TYPE) production capacity cannot be exceeded
POOL (PDREGION) total hogs available for shipment for each production region
PROCESSCAP (PCREGION) processing capacity cannot be exceeded
CONVERT (PCREGION) convert head to cwt
PROCESS (PCREGION) convert live hogs to pork
MEETDEM (MARKET) market demand must be met
OBJECTIVE objective function;
PRODUCTCAP (PDREGION, TYPE). LIVEPROD (PDREGION,TYPE) =L=
PRODCAP(PDREGION,TYPE);
POOL(PDREGION).. SUM (TYPE,LIVEPROD(PDREGION,TYPE)) =G=
SUM(PCREGION,LIVESHIP(PDREGION,PCREGION));
CONVERT (PCREGION). SUM (PDREGION, LIVESHIP(PDREGION,PCREGION))
=G=TOTALPROC(PCREGION);
PROCESSCAP(PCREGION).. TOTALPROC (PCREGION) =L= PROCCAP (PCREGION);
PROCESS (PCREGION). CF1*CF2* TOTALPROC (PCREGION) =G= SUM (MARKET, PORKSHIP
(PCREGION. MARKET»;

206

 

MEETDEM (MARKET). CF3*SUM (PCREGION, PORKSHIP (PCREGION. MARKET» =G=
DEMAND (MARKET);
OBJECTIVE.
SUM((PDREGION,TYPE),L1VEPROD(PDREGION,TYPE)*PRODCOST(PDREGlON,TYPE))
+
SUM((PDREGlON,PCREGION).LlVESHIP(PDREGION,PCREGION)*TC1(PDREGION,PCREGION))
+ SUM(PCREGION,TOTALPROC(PCREGION)*PROCCOST(PCREGION»
+
SUM((PCREGION,MARKET),PORKSHIP(PCREGlON,MARKET)*TC2(PCREGION,MARKET))
=E= TOTCOST;
MODEL TRANSHIP
/ ALL /;
SOLVE TRANSHIP USING LP MlNlMlZlNG TOTCOST;
DISPLAY
TCl, TC2, LIVEPRODL, LIVESHIPL, TOTALPROCL. PORKSHIPL, TOTCOSTL;
SCALAR C2 flexibility of demand for pork relative to pork price
/ -O.7804 / ;
SCALAR C1 shift parameter for demand (included all factors except pork price)
/ 0.996 / ;
PARAMETER PRICE (MARKET) calculated iterated price;
PRICE (MARKET) = 2.31;
*/Retail price of pork in 1997 was $2.31. The model starts with this price in the first iteration. 1n the
second iteration it will take the shadow price of each market as market price and re-estimates quantity
demanded. The process iterates 20 times,m
SET N /1*20/;
SCALAR dif/U;
PARAMETER balance (N, *);
LOOP (N$(dif> 0.001),
DEMAND (MARKET) = Cl‘DEMAND (MARKET)*(((1.75*MEETDEM.M (MARKET)/1000) / PRICE
(MARKET»"‘*C2);
PRICE (MARKET) = 1.75*MEETDEM.M (MARKED/1000;
*75% markup assumed
SOLVE TRANSHIP USING LP MlNlMlZlNG TOTCOST;
dif = sum(MARKET,ABS(PRICE(MARKET) - MEETDEM.M(MARKET)));
balance (N."D1FF PR") = dif; ) ;
DISPLAY balance, PRICE, DEMAND,
LIVEPRODL, LIVESHIPL. TOTALPROCL, PORKSHIPL. TOTCOSTL;

 

Demand estimation iteration

From Chapter Four, pork demand is estimated by the equation

Ram q, = r, + Z “A In P] + 5.09
j=l

In 1997, moving average of pork share = 0.2443, change in prices of beef, chicken and

fish are 0, —0.02 and 0 respectively, DQ= -0.0013. Parameters for the variables are listed
in Table 4.3

Aan, =CI+CZA1nP, ...................................... (a)
Where A In Q, = Change in per capita pork consumption in time t

CI = Other component of demand equation, related to cross price and income
C 2 = Coefficient related to pork price

A In P = Change in pork price

Simplification of equation (a) with little algebra:
an, -an,_I =C1 +C2(1nP, —lnP,_,)
1nQ,/1nQ,_I = (71+ C2(lnP, /lnP,_,)

(Q, /Q,-.) = e(C,)* e{C2(lnP, (In 13-1)}
(Q1/Q,-1)= e * (C1)* {(13 ”3-111“

Q, = 0996* {(P, /P,-.>}“”8 * Q.-.

 

Appendix 8

Appendix 8.1 Production levels and shadow prices in optimal solution (1,000 hogs)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

State Production level Shadow State Production level Shadow
Level Upper Price Size Level Upper Price
AL Small 20.328 74.952 0 NE Small 2304.486 2304.486 —18.424
AL Medium 74.952 74.952 -23.55 NE Medium 1854.831 1854.831 -44.234
AL Large 190.787 190.787 3229 NE Large 1461.381 1461.381 -57.164
AR Small 0 111.5 0 NV Small 3.938 3.938 -79.14
AR Medium 435.134 466.275 0 NV Medium 3.938 3.938 -108.41
AR Large 435.866 435.866 -10.6 NV Large 10.024 10.024 -122.15
AZ Small 78.195 78.195 -1.45 NM Small 0 1.956 0
AZ Medium 78.195 78.195 -30.85 NM Medium 1.956 1.956 -7.37
AZ Large 199.039 199.039 -44.59 NM Large 4.977 4.977 -20.84
CA Small 72.097 72.097 -59.895 NY Small 25.993 25.993 -14.28
CA Medium 72.097 72.097 -89.465 NY Medium 25.993 25.993 -33.18
CA Large 183.521 183.521 -103.245 NY Large 66.163 66.163 4358
CO Small 295.61 1 0 NC Small 0 294.721 0
CO Medium 295.61 1 0 NC Medium 2650.73 3831.379 0
CO Large 445.919 752.465 0 NC Large 10609.97 10609.97 - 10.59
FL Small 0 22.767 0 ND Small 1 1.014 64.36 0
FL Medium 0 22.767 0 ND Medium 64.36 64.36 -25.75
FL Large 0 57.953 0 ND Large 163.826 163.826 -38.7
GA Small 0 227.716 0 OH Small 771.777 1511.378 0
GA Medium 0 326.723 0 OH Medium 1096.49 1096.49 -20.88
GA Large 435.631 435.631 -4.9 OH Large 355.618 355.618 -33.61
1A Small 6444.004 6444.004 -3 .894 OK Small 0 176.845 0
1A Medium 9190.628 9190.628 -29.694 OK Medium 0 353.689 0
1A Large 5493.249 5493.249 —42.634 OK Large 2416.874 2416.874 -1.1 15
1D Small 0 15.004 0 OR Small 13 .947 -8.07 0
1D Medium 15.004 15.004 -3.67 OR Medium 13.947 -38.83 0
1D Large 38.192 38.192 -17.43 OR Large 35.501 -52.47 0
1L Small 2384.435 2384.435 -22.859 PA Small 374.617 374.617 -9.905
1L Medium 3034.735 3034.735 -43.829 PA Medium 638.236 638.236 -28.785
1L Large 24.39 24.39 -56.589 PA Large 374.617 374.617 -39.135
IN Small 1861.041 1861.041 -2.61 SC Small 0 106.567 0
IN Medium 2521.409 2521 .409 -23.59 SC Medium 106.567 106.567 -11.705
1N Large 1620.906 1620.906 -36.35 SC Large 271.263 271.263 -23.335
KS Small 0 735.594 0 SD Small 847.39 847.39 -23.029
KS Medium 0 676.747 0 SD Medium 533.542 533.542 -48.649
KS Large 79.126 1530.036 0 SD Large 711.389 711.389 -61 .559
KY Small 337.17 337.17 -20.325 TN Small 132.707 132.707 -1.785
KY Medium 388.256 388.256 -42.305 TN Medium 132.707 132.707 -23.945
KY Large 296.301 296.301 ~52.895 TN Large 337.799 337.799 -34.545

 

209

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

State Production level Shadow State Production level Shadow
Level Upper Price Size Level Upper Price
LA Small 0 12.678 0 TX Small 0 182.438 0
LA Medium 12.678 12.678 -1.055 TX Medium 0 182.438 0
LA Large 32.271 32.271 -12.705 TX Large 208 464.387 0
MD Small 40.5 40.5 -13.555 UT Small 0 55.582 0
MD Medium 40.5 40.5 -35.795 UT Medium 55.582 55.582 -23.675
MD Large 103.091 103.091 -46.415 UT Large 141.483 141.483 -37.415
Ml Small 0 420.916 0 VA Small 122.706 122.706 -4.515
MI Medium 670.348 670.348 -16.09 VA Medium 122.706 122.706 -25.675
M1 Large 467.684 467.684 -28.83 VA Large 312.344 312.344 -37.335
MN Small 2427.565 2427.565 -9.694 WA Small 11.019 11.019 -3.75
MN Medium 3236.753 3236.753 -30.574 WA Medium 11.019 11.019 -33.39
MN Large 2427.565 2427.565 -43.354 WA Large 28.049 28.049 -47.2
MS Small 0 90.296 0 W1 Small 0 794.449 0
MS Medium 90.296 90.296 -20.42 W1 Medium 539.09 539.09 -12.439
MS Large 229.844 229.844 -30.99 W1 Large 846.519 846.519 -25.129
MO Small 1260.459 1260.459 -27.819 WY Small 0 49.676 0
MO Medium 1375.046 1375.046 -48.719 WY Medium 0 49.676 0
MO Large 3093 .854 3093 .854 -60.379 WY Large 126.447 126.447 -1.58
MT Small 0 52.254 0 NH Small 0 9.285 0
MT Medium 0 52.254 0 NH Medium 0 9.285 0
MT Large 18.875 133.01 0 NH Large 0 23.635 0

 

 

 

 

 

210

 

 

 

Appendix 8.2 Pork processing locations and destinations (pork flow in solution)

Processing
R .

AR
CA
IL
KY
MN
MS
NC
OK
SC
SD
TN

IA
ID
IL
IN
MO
NE

AR
IA
IN

KY

MN

MS

NE

NC

VA

CA
1A
MO
NE
OK
PA
SD
VA

1N

NE
NC
ND

903 .049

1204.318

3672.312

1965.540
45.607

2943 .205

3719.667

AR
479.383

793
IA
1648.837

1992.937

328.841

ND

348.250

Markets 11
AZ CA

1658.264

10216.24

1583.729

1463.506

ID IL IN

6682.970
257.725

3198.258

151.058
MD MI MN

5144.74 275.679

2656.628

2130.786
349.682
NE NV
401.951

944.223
638.915

OH OK
5938.202
1689.215

211

3057.622
2317.468

193.701

1 189.5

KS
1279.548

137.070

MS

1372.932

NY

4358.851

1385.314

436.608
PA

4148.480

 

 

218.075
835.666
TN TX UT
2744.518

634.4
3580.924
4071.724 729.158

1838.792
949.356
408.743
317.2
3385.323
WI WY NH CT DC
1899.642
180.642 1112.877

545.529

 

1840.671

DE MA RI
327.861
1467.05
562.446 404.586 194.825
2765.875
908.050

 

Appendix 8.3 Pig flow from production locations to processing (1,000 hogs)

Production Process'
' CA 1A

AR

AZ 355.429

CA 327.715

IA 13429.36

ID

IL 5443.56

IN 4880.083
Ml 1138.032
MO 1361.359

MT

NV 17.90

NM 6.933

OH 1261.885
UT 197.065

WY 126.447

 

212

 

219.072
1 123.273

1021.727

6985.891

336.874

735.609

OH

1071.992

2092.321

213

MS
286.067
435.631

603.213

4200.244

557.756

1529.302

5620.698

184.091
118.149
314.655

1387.47

 

 

Appendix 8.4 Production levels in the base and projected model (1,000 hogs)

1997 Base
odel

010 Scenario
Sens '

161
9
2703
4414
521
2028
266
429
1781
2092
1804
23083
1525
435
4834
1294
10146
328
583
13756
76
1369
382
7150
1138
208
862
1074
398
5621
8503
47
402
89
32

99
0
0
0

214

gein

level
161
9
2696
4335
503
1910
247
366
1461
1714
1449
17080
1089
238
2610
691
4702
144
255
5664
23
347
96
1529
0
0

 

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